Datasets:

dylanhogg

/

awesome-python

like 0

util

https://github.com/lcompilers/lpython

[]

[]

[]

lcompilers/lpython

lpython

C++

https://lpython.org/

Python compiler

lcompilers

2024-01-12

2021-12-29

https://avatars.githubusercontent.com/u/96538276?v=4

Python compiler

['compiler', 'high-performance']

['compiler', 'high-performance']

2024-01-11

[('exaloop/codon', 0.7257847189903259, 'perf', 2), ('cython/cython', 0.6913489699363708, 'util', 0), ('pypy/pypy', 0.6041545271873474, 'util', 1), ('numba/numba', 0.603155255317688, 'perf', 1), ('pyston/pyston', 0.6024731397628784, 'util', 0), ('klen/py-frameworks-bench', 0.587522566318512, 'perf', 0), ('markshannon/faster-cpython', 0.5289682149887085, 'perf', 0), ('faster-cpython/tools', 0.5253562927246094, 'perf', 0), ('numba/llvmlite', 0.5191760063171387, 'util', 0), ('fastai/fastcore', 0.5177335739135742, 'util', 0), ('pympler/pympler', 0.5168886184692383, 'perf', 0), ('p403n1x87/austin', 0.5144882798194885, 'profiling', 0), ('benfred/py-spy', 0.5141026377677917, 'profiling', 0), ('pyutils/line_profiler', 0.5114628076553345, 'profiling', 0), ('joblib/joblib', 0.5082236528396606, 'util', 0), ('google/jax', 0.5010378956794739, 'ml', 0)]

llm

https://github.com/hao-ai-lab/lookaheaddecoding

['decoding', 'lookahead']

Break the Sequential Dependency of LLM Inference Using Lookahead Decoding

[]

[]

hao-ai-lab/lookaheaddecoding

LookaheadDecoding

Python

hao-ai-lab

2024-01-14

2023-11-21

https://avatars.githubusercontent.com/u/149045815?v=4

Break the Sequential Dependency of LLM Inference Using Lookahead Decoding

[]

['decoding', 'lookahead']

2024-01-09

[('karpathy/llama2.c', 0.5412218570709229, 'llm', 0), ('facebookresearch/llama', 0.5299458503723145, 'llm', 0), ('artidoro/qlora', 0.5174603462219238, 'llm', 0), ('facebookresearch/codellama', 0.5007719397544861, 'llm', 0)]

ml

https://github.com/ageron/handson-ml2

[]

[]

[]

ageron/handson-ml2

handson-ml2

Jupyter Notebook

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ageron

2024-01-14

2019-01-08

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

[]

[]

2023-02-04

[('fchollet/deep-learning-with-python-notebooks', 0.8291416168212891, 'study', 0), ('jakevdp/pythondatasciencehandbook', 0.6813152432441711, 'study', 0), ('gradio-app/gradio', 0.6595058441162109, 'viz', 0), ('rasbt/machine-learning-book', 0.6461301445960999, 'study', 0), ('firmai/industry-machine-learning', 0.6424956321716309, 'study', 0), ('mrdbourke/pytorch-deep-learning', 0.633056104183197, 'study', 0), ('wesm/pydata-book', 0.6252664923667908, 'study', 0), ('pytorch/ignite', 0.6152977347373962, 'ml-dl', 0), ('probml/pyprobml', 0.6119535565376282, 'ml', 0), ('scikit-learn/scikit-learn', 0.6084038615226746, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.6076744198799133, 'perf', 0), ('ddbourgin/numpy-ml', 0.6011914014816284, 'ml', 0), ('uber/petastorm', 0.5999022126197815, 'data', 0), ('skorch-dev/skorch', 0.5958633422851562, 'ml-dl', 0), ('jupyter/nbformat', 0.5934438705444336, 'jupyter', 0), ('cohere-ai/notebooks', 0.5897052884101868, 'llm', 0), ('d2l-ai/d2l-en', 0.5854299664497375, 'study', 0), ('xl0/lovely-tensors', 0.583730936050415, 'ml-dl', 0), ('jeshraghian/snntorch', 0.5824640989303589, 'ml-dl', 0), ('determined-ai/determined', 0.5803431868553162, 'ml-ops', 0), ('keras-team/keras', 0.5698571801185608, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5652621388435364, 'ml-dl', 0), ('ggerganov/ggml', 0.5649846196174622, 'ml', 0), ('tensorlayer/tensorlayer', 0.5648738741874695, 'ml-rl', 0), ('rasbt/mlxtend', 0.564663290977478, 'ml', 0), ('tensorly/tensorly', 0.5573654770851135, 'ml-dl', 0), ('tensorflow/tensorflow', 0.5564263463020325, 'ml-dl', 0), ('kubeflow/fairing', 0.5555180907249451, 'ml-ops', 0), ('pytorch/pytorch', 0.5553191304206848, 'ml-dl', 0), ('udacity/deep-learning-v2-pytorch', 0.5552393198013306, 'study', 0), ('featurelabs/featuretools', 0.5548660159111023, 'ml', 0), ('numpy/numpy', 0.5535714626312256, 'math', 0), ('huggingface/huggingface_hub', 0.5522385835647583, 'ml', 0), ('ipython/ipykernel', 0.55137699842453, 'util', 0), ('mdbloice/augmentor', 0.5503374338150024, 'ml', 0), ('graykode/nlp-tutorial', 0.5493988394737244, 'study', 0), ('tensorflow/lucid', 0.5488042235374451, 'ml-interpretability', 0), ('tensorflow/tensor2tensor', 0.5483447313308716, 'ml', 0), ('dmlc/dgl', 0.5474348068237305, 'ml-dl', 0), ('patchy631/machine-learning', 0.5471069812774658, 'ml', 0), ('lightly-ai/lightly', 0.5470166802406311, 'ml', 0), ('kubeflow-kale/kale', 0.5455378293991089, 'ml-ops', 0), ('ipython/ipyparallel', 0.5427348017692566, 'perf', 0), ('pycaret/pycaret', 0.5426246523857117, 'ml', 0), ('keras-rl/keras-rl', 0.5413088202476501, 'ml-rl', 0), ('huggingface/transformers', 0.5403335094451904, 'nlp', 0), ('udlbook/udlbook', 0.539566159248352, 'study', 0), ('jupyter/nbconvert', 0.5394284725189209, 'jupyter', 0), ('tlkh/tf-metal-experiments', 0.5388534665107727, 'perf', 0), ('aws/sagemaker-python-sdk', 0.5365365743637085, 'ml', 0), ('gerdm/prml', 0.5364505648612976, 'study', 0), ('arogozhnikov/einops', 0.5353853702545166, 'ml-dl', 0), ('wandb/client', 0.5327136516571045, 'ml', 0), ('automl/auto-sklearn', 0.531454861164093, 'ml', 0), ('scikit-learn-contrib/metric-learn', 0.5313714146614075, 'ml', 0), ('tensorflow/addons', 0.5312256813049316, 'ml', 0), ('dylanhogg/awesome-python', 0.5303460955619812, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5303263068199158, 'study', 0), ('goldmansachs/gs-quant', 0.5294873118400574, 'finance', 0), ('koaning/human-learn', 0.5293908715248108, 'data', 0), ('keras-team/keras-nlp', 0.528778076171875, 'nlp', 0), ('cerlymarco/medium_notebook', 0.525178074836731, 'study', 0), ('xl0/lovely-numpy', 0.52412348985672, 'util', 0), ('pyro-ppl/pyro', 0.522969663143158, 'ml-dl', 0), ('adafruit/circuitpython', 0.5219725966453552, 'util', 0), ('rafiqhasan/auto-tensorflow', 0.5208608508110046, 'ml-dl', 0), ('jupyter/notebook', 0.5203615427017212, 'jupyter', 0), ('skops-dev/skops', 0.5202804207801819, 'ml-ops', 0), ('epistasislab/tpot', 0.5202245712280273, 'ml', 0), ('aws/graph-notebook', 0.519343912601471, 'jupyter', 0), ('ta-lib/ta-lib-python', 0.5182675123214722, 'finance', 0), ('horovod/horovod', 0.5166817307472229, 'ml-ops', 0), ('rasbt/stat451-machine-learning-fs20', 0.5153390169143677, 'study', 0), ('koaning/scikit-lego', 0.5152604579925537, 'ml', 0), ('pyg-team/pytorch_geometric', 0.5134172439575195, 'ml-dl', 0), ('huggingface/datasets', 0.5119935870170593, 'nlp', 0), ('explosion/thinc', 0.5114274621009827, 'ml-dl', 0), ('scikit-learn-contrib/lightning', 0.5109522938728333, 'ml', 0), ('jupyterlab/jupyterlab-desktop', 0.510633647441864, 'jupyter', 0), ('nicolas-chaulet/torch-points3d', 0.5103498101234436, 'ml', 0), ('jupyter/nbgrader', 0.5094665884971619, 'jupyter', 0), ('quantopian/qgrid', 0.5087634921073914, 'jupyter', 0), ('google/gin-config', 0.5084095001220703, 'util', 0), ('python/cpython', 0.5082079172134399, 'util', 0), ('microsoft/flaml', 0.5074008107185364, 'ml', 0), ('pypy/pypy', 0.5072569251060486, 'util', 0), ('eleutherai/pyfra', 0.5071380734443665, 'ml', 0), ('tatsu-lab/stanford_alpaca', 0.5068382620811462, 'llm', 0), ('jupyter-widgets/ipywidgets', 0.5040411949157715, 'jupyter', 0), ('realpython/python-guide', 0.5025880336761475, 'study', 0), ('iryna-kondr/scikit-llm', 0.5018590688705444, 'llm', 0), ('jupyterlab/jupyterlab', 0.5005123019218445, 'jupyter', 0), ('google/vizier', 0.5004328489303589, 'ml', 0)]

ml-dl

https://github.com/facebookresearch/detectron

[]

[]

[]

facebookresearch/detectron

Detectron

Python

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

facebookresearch

2024-01-14

2017-10-05

https://avatars.githubusercontent.com/u/16943930?v=4

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

[]

[]

2023-10-19

[('matterport/mask_rcnn', 0.5304756760597229, 'ml-dl', 0), ('open-mmlab/mmdetection', 0.5015984177589417, 'ml', 0)]

ml

https://github.com/jindongwang/transferlearning

[]

[]

[]

jindongwang/transferlearning

transferlearning

Python

http://transferlearning.xyz/

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

jindongwang

2024-01-14

2017-04-30

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

['deep-learning', 'domain-adaptation', 'domain-adaption', 'domain-generalization', 'few-shot', 'few-shot-learning', 'generalization', 'machine-learning', 'meta-learning', 'paper', 'papers', 'representation-learning', 'self-supervised-learning', 'style-transfer', 'survey', 'theory', 'transfer-learning', 'transferlearning', 'tutorial-code', 'unsupervised-learning']

['deep-learning', 'domain-adaptation', 'domain-adaption', 'domain-generalization', 'few-shot', 'few-shot-learning', 'generalization', 'machine-learning', 'meta-learning', 'paper', 'papers', 'representation-learning', 'self-supervised-learning', 'style-transfer', 'survey', 'theory', 'transfer-learning', 'transferlearning', 'tutorial-code', 'unsupervised-learning']

2024-01-08

[('amanchadha/coursera-deep-learning-specialization', 0.5513812899589539, 'study', 1), ('huggingface/autotrain-advanced', 0.5214440822601318, 'ml', 2), ('patchy631/machine-learning', 0.5060864090919495, 'ml', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5019001364707947, 'study', 2), ('udacity/deep-learning-v2-pytorch', 0.501124918460846, 'study', 2), ('awslabs/autogluon', 0.5002632141113281, 'ml', 3)]

ml-dl

https://github.com/facebookresearch/detr

[]

[]

[]

facebookresearch/detr

detr

Python

End-to-End Object Detection with Transformers

facebookresearch

2024-01-14

2020-05-26

https://avatars.githubusercontent.com/u/16943930?v=4

End-to-End Object Detection with Transformers

[]

[]

2023-02-07

[('cvg/lightglue', 0.5226452350616455, 'ml-dl', 0), ('nvlabs/gcvit', 0.5166937112808228, 'diffusion', 0), ('matterport/mask_rcnn', 0.5123329758644104, 'ml-dl', 0)]

ml

https://github.com/microsoft/swin-transformer

[]

[]

[]

microsoft/swin-transformer

Swin-Transformer

Python

https://arxiv.org/abs/2103.14030

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

microsoft

2024-01-14

2021-03-25

https://avatars.githubusercontent.com/u/6154722?v=4

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

['ade20k', 'image-classification', 'imagenet', 'mask-rcnn', 'mscoco', 'object-detection', 'semantic-segmentation', 'swin-transformer']

['ade20k', 'image-classification', 'imagenet', 'mask-rcnn', 'mscoco', 'object-detection', 'semantic-segmentation', 'swin-transformer']

2023-08-16

[('nvlabs/gcvit', 0.6548908352851868, 'diffusion', 4), ('google-research/maxvit', 0.5897934436798096, 'ml', 1), ('lucidrains/vit-pytorch', 0.5670905113220215, 'ml-dl', 1), ('open-mmlab/mmdetection', 0.5538708567619324, 'ml', 3), ('deci-ai/super-gradients', 0.5506076812744141, 'ml-dl', 4), ('open-mmlab/mmsegmentation', 0.5363519191741943, 'ml', 2), ('hrnet/hrnet-semantic-segmentation', 0.5201693177223206, 'ml', 1), ('roboflow/supervision', 0.5062756538391113, 'ml', 1)]

ml

https://github.com/statsmodels/statsmodels

[]

[]

[]

statsmodels/statsmodels

statsmodels

Python

http://www.statsmodels.org/devel/

Statsmodels: statistical modeling and econometrics in Python

statsmodels

2024-01-13

2011-06-12

https://avatars.githubusercontent.com/u/717666?v=4

Statsmodels: statistical modeling and econometrics in Python

['count-model', 'data-analysis', 'data-science', 'econometrics', 'forecasting', 'generalized-linear-models', 'hypothesis-testing', 'prediction', 'regression-models', 'robust-estimation', 'statistics', 'timeseries-analysis']

['count-model', 'data-analysis', 'data-science', 'econometrics', 'forecasting', 'generalized-linear-models', 'hypothesis-testing', 'prediction', 'regression-models', 'robust-estimation', 'statistics', 'timeseries-analysis']

2024-01-04

[('firmai/atspy', 0.6599208116531372, 'time-series', 1), ('ranaroussi/quantstats', 0.6285594701766968, 'finance', 0), ('alkaline-ml/pmdarima', 0.6218242645263672, 'time-series', 2), ('scikit-learn/scikit-learn', 0.6116586327552795, 'ml', 3), ('scikit-mobility/scikit-mobility', 0.5975031852722168, 'gis', 3), ('bashtage/arch', 0.5926198363304138, 'time-series', 1), ('plotly/dash', 0.5885524749755859, 'viz', 1), ('awslabs/gluonts', 0.5710023641586304, 'time-series', 2), ('goldmansachs/gs-quant', 0.5688977241516113, 'finance', 0), ('pymc-devs/pymc3', 0.5598034858703613, 'ml', 0), ('uber/orbit', 0.5589537024497986, 'time-series', 2), ('stan-dev/pystan', 0.5519436001777649, 'ml', 0), ('quantecon/quantecon.py', 0.5498467087745667, 'sim', 0), ('pandas-dev/pandas', 0.5481253266334534, 'pandas', 2), ('rjt1990/pyflux', 0.5408421158790588, 'time-series', 1), ('crflynn/stochastic', 0.5304479002952576, 'sim', 0), ('online-ml/river', 0.5298870205879211, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.5274232625961304, 'study', 3), ('rasbt/mlxtend', 0.5218582153320312, 'ml', 1), ('eleutherai/pyfra', 0.5143864154815674, 'ml', 0), ('polyaxon/datatile', 0.5121821165084839, 'pandas', 2), ('wesm/pydata-book', 0.5118904709815979, 'study', 0), ('cuemacro/finmarketpy', 0.5101310610771179, 'finance', 0)]

util

https://github.com/facebookresearch/hydra

[]

[]

[]

facebookresearch/hydra

hydra

Python

https://hydra.cc

Hydra is a framework for elegantly configuring complex applications

facebookresearch

2024-01-14

2019-06-12

https://avatars.githubusercontent.com/u/16943930?v=4

Hydra is a framework for elegantly configuring complex applications

[]

[]

2023-11-30

[('ashleve/lightning-hydra-template', 0.5850319266319275, 'util', 0), ('google/gin-config', 0.5556942224502563, 'util', 0), ('willmcgugan/textual', 0.5213847160339355, 'term', 0), ('alphasecio/langchain-examples', 0.5024363994598389, 'llm', 0)]

util

https://github.com/xonsh/xonsh

['shell']

[]

[]

xonsh/xonsh

xonsh

Python

http://xon.sh

:shell: Python-powered, cross-platform, Unix-gazing shell.

xonsh

2024-01-14

2015-01-21

https://avatars.githubusercontent.com/u/17418188?v=4

:shell: Python-powered, cross-platform, Unix-gazing shell.

['bash', 'cli', 'command-line', 'console', 'devops', 'fish', 'iterm2', 'prompt', 'python-shell', 'script', 'shell', 'terminal', 'windows-terminal', 'xonsh', 'zsh']

['bash', 'cli', 'command-line', 'console', 'devops', 'fish', 'iterm2', 'prompt', 'python-shell', 'script', 'shell', 'terminal', 'windows-terminal', 'xonsh', 'zsh']

2023-12-31

[('tiangolo/typer', 0.614140510559082, 'term', 3), ('kellyjonbrazil/jc', 0.5756747722625732, 'util', 3), ('jquast/blessed', 0.569438099861145, 'term', 2), ('pygamelib/pygamelib', 0.5624502301216125, 'gamedev', 0), ('urwid/urwid', 0.5422582030296326, 'term', 0), ('pypy/pypy', 0.5222761631011963, 'util', 0), ('tmbo/questionary', 0.5194598436355591, 'term', 1), ('python/cpython', 0.5193564891815186, 'util', 0), ('federicoceratto/dashing', 0.5140331387519836, 'term', 1), ('google/python-fire', 0.5076464414596558, 'term', 1), ('evhub/coconut', 0.5075111985206604, 'util', 1), ('hoffstadt/dearpygui', 0.504555881023407, 'gui', 0), ('cython/cython', 0.5044435262680054, 'util', 0)]

util

https://github.com/googleapis/google-api-python-client

[]

[]

[]

googleapis/google-api-python-client

google-api-python-client

Python

https://googleapis.github.io/google-api-python-client/docs/

🐍 The official Python client library for Google's discovery based APIs.

googleapis

2024-01-13

2014-01-08

https://avatars.githubusercontent.com/u/16785467?v=4

🐍 The official Python client library for Google's discovery based APIs.

[]

[]

2024-01-09

[('nv7-github/googlesearch', 0.6702570915222168, 'util', 0), ('dsdanielpark/bard-api', 0.6036682724952698, 'llm', 0), ('openai/openai-python', 0.5968145728111267, 'util', 0), ('dialogflow/dialogflow-python-client-v2', 0.5611771941184998, 'nlp', 0), ('radiantearth/radiant-mlhub', 0.5603718757629395, 'gis', 0), ('typesense/typesense-python', 0.5588173866271973, 'data', 0), ('snyk-labs/pysnyk', 0.5522992610931396, 'security', 0), ('psf/requests', 0.552095353603363, 'web', 0), ('jovianml/opendatasets', 0.551531970500946, 'data', 0), ('meilisearch/meilisearch-python', 0.5505169034004211, 'data', 0), ('googleapis/python-speech', 0.5471388101577759, 'ml', 0), ('pytoolz/toolz', 0.5453507304191589, 'util', 0), ('urwid/urwid', 0.54488605260849, 'term', 0), ('simple-salesforce/simple-salesforce', 0.541388213634491, 'data', 0), ('qdrant/qdrant-client', 0.5371494293212891, 'util', 0), ('giswqs/geemap', 0.5369963645935059, 'gis', 0), ('scholarly-python-package/scholarly', 0.5367330312728882, 'data', 0), ('requests/toolbelt', 0.5348877906799316, 'util', 0), ('clips/pattern', 0.5339103937149048, 'nlp', 0), ('fastai/ghapi', 0.5308494567871094, 'util', 0), ('huggingface/huggingface_hub', 0.5284603238105774, 'ml', 0), ('shishirpatil/gorilla', 0.524009108543396, 'llm', 0), ('goldsmith/wikipedia', 0.5227052569389343, 'data', 0), ('mitmproxy/pdoc', 0.5224719047546387, 'util', 0), ('hydrosquall/tiingo-python', 0.5202198624610901, 'finance', 0), ('pndurette/gtts', 0.5190337300300598, 'util', 0), ('landscapeio/prospector', 0.5145336389541626, 'util', 0), ('googleapis/python-bigquery', 0.5121608376502991, 'data', 0), ('amaargiru/pyroad', 0.5120947957038879, 'study', 0), ('hugapi/hug', 0.5113233327865601, 'util', 0), ('serpapi/google-search-results-python', 0.5091744065284729, 'util', 0), ('scrapy/scrapy', 0.5074482560157776, 'data', 0), ('cohere-ai/cohere-python', 0.5053079128265381, 'util', 0), ('1200wd/bitcoinlib', 0.5004116892814636, 'crypto', 0)]

ml

https://github.com/lmcinnes/umap

[]

[]

[]

lmcinnes/umap

umap

Python

Uniform Manifold Approximation and Projection

lmcinnes

2024-01-14

2017-07-02

Uniform Manifold Approximation and Projection

['dimensionality-reduction', 'machine-learning', 'topological-data-analysis', 'umap', 'visualization']

['dimensionality-reduction', 'machine-learning', 'topological-data-analysis', 'umap', 'visualization']

2024-01-08

[('geomstats/geomstats', 0.5977250933647156, 'math', 1)]

util

https://github.com/pycqa/isort

['code-quality']

[]

[]

pycqa/isort

isort

Python

https://pycqa.github.io/isort/

A Python utility / library to sort imports.

pycqa

2024-01-14

2013-09-02

https://avatars.githubusercontent.com/u/8749848?v=4

A Python utility / library to sort imports.

['auto-formatter', 'cleaner', 'cli', 'formatter', 'isort', 'linter', 'python-utility', 'sorting-imports']

['auto-formatter', 'cleaner', 'cli', 'code-quality', 'formatter', 'isort', 'linter', 'python-utility', 'sorting-imports']

2024-01-12

[('hadialqattan/pycln', 0.6549221277236938, 'util', 0), ('google/yapf', 0.5961623191833496, 'util', 2), ('asottile/reorder-python-imports', 0.5951371192932129, 'util', 2), ('landscapeio/prospector', 0.574863851070404, 'util', 0), ('sethmmorton/natsort', 0.5276709794998169, 'util', 0), ('google/pytype', 0.5101639032363892, 'typing', 2), ('hhatto/autopep8', 0.5075655579566956, 'util', 1), ('grantjenks/blue', 0.5017447471618652, 'util', 2)]

study

https://github.com/zhanymkanov/fastapi-best-practices

[]

[]

[]

zhanymkanov/fastapi-best-practices

fastapi-best-practices

FastAPI Best Practices and Conventions we used at our startup

zhanymkanov

2024-01-14

2022-08-09

FastAPI Best Practices and Conventions we used at our startup

['best-practices', 'fastapi']

['best-practices', 'fastapi']

2023-10-22

[('fastapi-users/fastapi-users', 0.6014936566352844, 'web', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5217467546463013, 'template', 1), ('dmontagu/fastapi_client', 0.5196253061294556, 'web', 0), ('tiangolo/fastapi', 0.5182605981826782, 'web', 1)]

time-series

https://github.com/facebookresearch/kats

['time-series']

[]

[]

facebookresearch/kats

Kats

Python

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

facebookresearch

2024-01-14

2021-02-25

https://avatars.githubusercontent.com/u/16943930?v=4

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

[]

['time-series']

2024-01-10

[('sktime/sktime', 0.5305997729301453, 'time-series', 1), ('alkaline-ml/pmdarima', 0.5154160261154175, 'time-series', 1), ('salesforce/merlion', 0.5117724537849426, 'time-series', 1)]

ml-ops

https://github.com/aimhubio/aim

[]

[]

[]

aimhubio/aim

aim

Python

https://aimstack.io

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

aimhubio

2024-01-13

2019-05-31

https://avatars.githubusercontent.com/u/51399196?v=4

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

['ai', 'data-science', 'data-visualization', 'experiment-tracking', 'machine-learning', 'metadata', 'metadata-tracking', 'ml', 'mlflow', 'mlops', 'prompt-engineering', 'pytorch', 'tensorboard', 'tensorflow', 'visualization']

['ai', 'data-science', 'data-visualization', 'experiment-tracking', 'machine-learning', 'metadata', 'metadata-tracking', 'ml', 'mlflow', 'mlops', 'prompt-engineering', 'pytorch', 'tensorboard', 'tensorflow', 'visualization']

2024-01-12

[('wandb/client', 0.696733832359314, 'ml', 5), ('polyaxon/datatile', 0.6386370062828064, 'pandas', 5), ('determined-ai/determined', 0.614514172077179, 'ml-ops', 5), ('netflix/metaflow', 0.5948770046234131, 'ml-ops', 5), ('mlflow/mlflow', 0.5842969417572021, 'ml-ops', 4), ('iterative/dvc', 0.5769718885421753, 'ml-ops', 3), ('whylabs/whylogs', 0.5764767527580261, 'util', 3), ('salesforce/logai', 0.5716543197631836, 'util', 2), ('microsoft/onnxruntime', 0.5700400471687317, 'ml', 3), ('transformeroptimus/superagi', 0.5632416605949402, 'llm', 1), ('polyaxon/polyaxon', 0.5590616464614868, 'ml-ops', 6), ('activeloopai/deeplake', 0.5577347874641418, 'ml-ops', 7), ('nebuly-ai/nebullvm', 0.5485852360725403, 'perf', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5477184653282166, 'study', 2), ('huggingface/datasets', 0.5470335483551025, 'nlp', 3), ('bentoml/bentoml', 0.5450233817100525, 'ml-ops', 3), ('tensorflow/tensorflow', 0.5438287258148193, 'ml-dl', 3), ('doccano/doccano', 0.5435435771942139, 'nlp', 1), ('prefecthq/marvin', 0.541201114654541, 'nlp', 1), ('sweepai/sweep', 0.5406528115272522, 'llm', 1), ('googlecloudplatform/vertex-ai-samples', 0.5386273264884949, 'ml', 4), ('oegedijk/explainerdashboard', 0.5350830554962158, 'ml-interpretability', 0), ('roboflow/supervision', 0.5213971138000488, 'ml', 3), ('argilla-io/argilla', 0.5164137482643127, 'nlp', 3), ('adap/flower', 0.5154430270195007, 'ml-ops', 4), ('merantix-momentum/squirrel-core', 0.5153231024742126, 'ml', 6), ('csinva/imodels', 0.5152265429496765, 'ml', 4), ('cheshire-cat-ai/core', 0.5146742463111877, 'llm', 1), ('tensorlayer/tensorlayer', 0.5126657485961914, 'ml-rl', 1), ('gradio-app/gradio', 0.5126237869262695, 'viz', 3), ('kedro-org/kedro-viz', 0.5116142630577087, 'ml-ops', 2), ('mlc-ai/mlc-llm', 0.509802520275116, 'llm', 0), ('bulletphysics/bullet3', 0.5082801580429077, 'sim', 0), ('pathwaycom/llm-app', 0.506819486618042, 'llm', 1), ('google-research/language', 0.5032878518104553, 'nlp', 1), ('fmind/mlops-python-package', 0.5028169751167297, 'template', 3), ('laion-ai/open-assistant', 0.5019581913948059, 'llm', 2), ('pytorchlightning/pytorch-lightning', 0.5019053220748901, 'ml-dl', 4), ('tlkh/tf-metal-experiments', 0.501725971698761, 'perf', 1)]

study

https://github.com/neetcode-gh/leetcode

['interview-questions', 'data-structures', 'leetcode']

Leetcode solutions for NeetCode.io

[]

[]

neetcode-gh/leetcode

leetcode

JavaScript

Leetcode solutions

neetcode-gh

2024-01-14

2021-01-20

Leetcode solutions

[]

['data-structures', 'interview-questions', 'leetcode']

2024-01-13

[('mdmzfzl/neetcode-solutions', 0.6274089217185974, 'study', 3)]

ml-interpretability

https://github.com/pytorch/captum

[]

[]

[]

pytorch/captum

captum

Python

https://captum.ai

Model interpretability and understanding for PyTorch

pytorch

2024-01-14

2019-08-27

https://avatars.githubusercontent.com/u/21003710?v=4

Model interpretability and understanding for PyTorch

['feature-attribution', 'feature-importance', 'interpretability', 'interpretable-ai', 'interpretable-ml']

['feature-attribution', 'feature-importance', 'interpretability', 'interpretable-ai', 'interpretable-ml']

2024-01-08

[('pytorch/ignite', 0.6821672916412354, 'ml-dl', 0), ('tensorflow/lucid', 0.6784272193908691, 'ml-interpretability', 1), ('csinva/imodels', 0.6309248208999634, 'ml', 1), ('skorch-dev/skorch', 0.6131489276885986, 'ml-dl', 0), ('interpretml/interpret', 0.6127338409423828, 'ml-interpretability', 3), ('mrdbourke/pytorch-deep-learning', 0.5934852361679077, 'study', 0), ('allenai/allennlp', 0.5909707546234131, 'nlp', 0), ('marcotcr/lime', 0.5743399262428284, 'ml-interpretability', 1), ('intel/intel-extension-for-pytorch', 0.5694814324378967, 'perf', 0), ('pair-code/lit', 0.5618991851806641, 'ml-interpretability', 0), ('nvidia/apex', 0.5534337759017944, 'ml-dl', 0), ('eleutherai/pythia', 0.5508965253829956, 'ml-interpretability', 2), ('xl0/lovely-tensors', 0.5507166385650635, 'ml-dl', 0), ('huggingface/transformers', 0.5457330942153931, 'nlp', 0), ('huggingface/accelerate', 0.5386924743652344, 'ml', 0), ('pytorch/data', 0.5359891057014465, 'data', 0), ('selfexplainml/piml-toolbox', 0.5324576497077942, 'ml-interpretability', 0), ('rasbt/machine-learning-book', 0.5297553539276123, 'study', 0), ('arogozhnikov/einops', 0.524075984954834, 'ml-dl', 0), ('ibm/transition-amr-parser', 0.5239638090133667, 'nlp', 0), ('hysts/pytorch_image_classification', 0.5230705142021179, 'ml-dl', 0), ('mosaicml/composer', 0.5189915299415588, 'ml-dl', 0), ('speechbrain/speechbrain', 0.5150958895683289, 'nlp', 0), ('pytorch/rl', 0.5139393210411072, 'ml-rl', 0), ('rafiqhasan/auto-tensorflow', 0.5135285258293152, 'ml-dl', 0), ('rentruewang/koila', 0.5131102800369263, 'ml', 0), ('salesforce/blip', 0.5107000470161438, 'diffusion', 0), ('ashleve/lightning-hydra-template', 0.5099735260009766, 'util', 0), ('pytorch/botorch', 0.509192168712616, 'ml-dl', 0), ('blackhc/toma', 0.5078623294830322, 'ml-dl', 0), ('seldonio/alibi', 0.5067712664604187, 'ml-interpretability', 1), ('cvxgrp/pymde', 0.5022547245025635, 'ml', 0)]

testing

https://github.com/seleniumbase/seleniumbase

[]

[]

[]

seleniumbase/seleniumbase

SeleniumBase

Python

https://seleniumbase.io

Browser automation framework for testing with Selenium, Python, and pytest. Includes a Dashboard, a Recorder for generating tests, Undetected Mode, and more.

seleniumbase

2024-01-13

2014-03-04

https://avatars.githubusercontent.com/u/17287301?v=4

Browser automation framework for testing with Selenium, Python, and pytest. Includes a Dashboard, a Recorder for generating tests, Undetected Mode, and more.

['behave', 'chrome', 'chromedriver', 'e2e-testing', 'firefox', 'pytest', 'pytest-plugin', 'selenium', 'selenium-python', 'seleniumbase', 'test', 'unittests', 'web-automation', 'webdriver', 'webkit']

['behave', 'chrome', 'chromedriver', 'e2e-testing', 'firefox', 'pytest', 'pytest-plugin', 'selenium', 'selenium-python', 'seleniumbase', 'test', 'unittests', 'web-automation', 'webdriver', 'webkit']

2024-01-04

[('cobrateam/splinter', 0.7703961730003357, 'testing', 2), ('microsoft/playwright-python', 0.6941218972206116, 'testing', 2), ('webpy/webpy', 0.5600611567497253, 'web', 0), ('taverntesting/tavern', 0.5573855042457581, 'testing', 1), ('bokeh/bokeh', 0.5535537004470825, 'viz', 0), ('masoniteframework/masonite', 0.5500764846801758, 'web', 0), ('alirezamika/autoscraper', 0.5415842533111572, 'data', 0), ('pyodide/pyodide', 0.540057897567749, 'util', 0), ('wolever/parameterized', 0.5371958613395691, 'testing', 0), ('clips/pattern', 0.5308951735496521, 'nlp', 0), ('plotly/dash', 0.5277555584907532, 'viz', 0), ('r0x0r/pywebview', 0.5268258452415466, 'gui', 1), ('jiffyclub/snakeviz', 0.5255619287490845, 'profiling', 0), ('robotframework/robotframework', 0.5185773968696594, 'testing', 0), ('pytest-dev/pytest-testinfra', 0.5065727233886719, 'testing', 1), ('roniemartinez/dude', 0.5045518279075623, 'util', 1), ('scrapy/scrapy', 0.5043920278549194, 'data', 0), ('pallets/flask', 0.5030795931816101, 'web', 0), ('voila-dashboards/voila', 0.5014607906341553, 'jupyter', 0)]

ml

https://github.com/sanchit-gandhi/whisper-jax

[]

[]

[]

sanchit-gandhi/whisper-jax

whisper-jax

Jupyter Notebook

JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.

sanchit-gandhi

2024-01-13

2023-03-02

JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.

['deep-learning', 'jax', 'speech-recognition', 'speech-to-text', 'whisper']

['deep-learning', 'jax', 'speech-recognition', 'speech-to-text', 'whisper']

2023-12-15

[('ggerganov/whisper.cpp', 0.6906029582023621, 'util', 3), ('deepmind/dm-haiku', 0.6133875846862793, 'ml-dl', 2), ('m-bain/whisperx', 0.5212621688842773, 'nlp', 3)]

crypto

https://github.com/ethereum/consensus-specs

[]

[]

[]

ethereum/consensus-specs

consensus-specs

Python

Ethereum Proof-of-Stake Consensus Specifications

ethereum

2024-01-12

2018-09-20

https://avatars.githubusercontent.com/u/6250754?v=4

Ethereum Proof-of-Stake Consensus Specifications

[]

[]

2024-01-11

[]

data

https://github.com/rom1504/img2dataset

[]

[]

[]

rom1504/img2dataset

img2dataset

Python

Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.

rom1504

2024-01-13

2021-08-11

Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.

['big-data', 'dataset', 'deep-learning', 'download-images', 'image', 'image-dataset', 'multimodal']

['big-data', 'dataset', 'deep-learning', 'download-images', 'image', 'image-dataset', 'multimodal']

2024-01-13

[('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5232915878295898, 'web', 0), ('aiqc/aiqc', 0.5084776282310486, 'ml-ops', 0), ('microsoft/deepspeed', 0.5075109601020813, 'ml-dl', 1)]

llm

https://github.com/iryna-kondr/scikit-llm

[]

[]

[]

iryna-kondr/scikit-llm

scikit-llm

Python

https://beastbyte.ai/

Seamlessly integrate LLMs into scikit-learn.

iryna-kondr

2024-01-12

2023-05-12

Seamlessly integrate LLMs into scikit-learn.

['chatgpt', 'deep-learning', 'llm', 'machine-learning', 'scikit-learn', 'transformers']

['chatgpt', 'deep-learning', 'llm', 'machine-learning', 'scikit-learn', 'transformers']

2023-12-25

[('microsoft/jarvis', 0.6588683128356934, 'llm', 1), ('alpha-vllm/llama2-accessory', 0.6441587209701538, 'llm', 0), ('tigerlab-ai/tiger', 0.6261765956878662, 'llm', 1), ('koaning/scikit-lego', 0.614821195602417, 'ml', 2), ('vllm-project/vllm', 0.6003293395042419, 'llm', 1), ('microsoft/semantic-kernel', 0.5926992893218994, 'llm', 1), ('bigscience-workshop/petals', 0.5923831462860107, 'data', 2), ('pathwaycom/llm-app', 0.5918958187103271, 'llm', 2), ('microsoft/torchscale', 0.5895041823387146, 'llm', 1), ('intel/scikit-learn-intelex', 0.5876615047454834, 'perf', 2), ('h2oai/h2o-llmstudio', 0.5840808749198914, 'llm', 2), ('argilla-io/argilla', 0.5834850668907166, 'nlp', 2), ('rasbt/machine-learning-book', 0.5804186463356018, 'study', 3), ('intel/intel-extension-for-transformers', 0.5800312757492065, 'perf', 0), ('nomic-ai/gpt4all', 0.5748793482780457, 'llm', 0), ('skops-dev/skops', 0.5726227760314941, 'ml-ops', 2), ('nebuly-ai/nebullvm', 0.5629363059997559, 'perf', 1), ('deepset-ai/haystack', 0.5621213912963867, 'llm', 3), ('bobazooba/xllm', 0.5620294213294983, 'llm', 3), ('ray-project/ray-llm', 0.5598890781402588, 'llm', 2), ('automl/auto-sklearn', 0.5549347996711731, 'ml', 1), ('microsoft/onnxruntime', 0.5525389313697815, 'ml', 3), ('hegelai/prompttools', 0.5520104765892029, 'llm', 2), ('explosion/spacy-llm', 0.550988495349884, 'llm', 2), ('microsoft/promptcraft-robotics', 0.5381367206573486, 'sim', 2), ('bentoml/openllm', 0.5351253151893616, 'ml-ops', 1), ('ludwig-ai/ludwig', 0.5349180698394775, 'ml-ops', 3), ('huggingface/transformers', 0.5332709550857544, 'nlp', 2), ('mooler0410/llmspracticalguide', 0.5321641564369202, 'study', 0), ('microsoft/promptflow', 0.5263214707374573, 'llm', 2), ('young-geng/easylm', 0.5262447595596313, 'llm', 1), ('koaning/human-learn', 0.5255882143974304, 'data', 2), ('night-chen/toolqa', 0.522339940071106, 'llm', 0), ('truera/trulens', 0.5199382901191711, 'llm', 2), ('horovod/horovod', 0.5187541842460632, 'ml-ops', 2), ('skorch-dev/skorch', 0.5175820589065552, 'ml-dl', 2), ('optimalscale/lmflow', 0.5123019814491272, 'llm', 2), ('onnx/onnx', 0.5105788111686707, 'ml', 3), ('llmware-ai/llmware', 0.5102970600128174, 'llm', 2), ('embedchain/embedchain', 0.5067012310028076, 'llm', 2), ('salesforce/xgen', 0.5054061412811279, 'llm', 1), ('agenta-ai/agenta', 0.5052707195281982, 'llm', 1), ('lightning-ai/lit-gpt', 0.5048611164093018, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5023376941680908, 'llm', 2), ('ageron/handson-ml2', 0.5018590688705444, 'ml', 0), ('determined-ai/determined', 0.5017955303192139, 'ml-ops', 2), ('databrickslabs/dolly', 0.5001913905143738, 'llm', 0)]

ml-dl

https://github.com/cvg/lightglue

[]

[]

[]

cvg/lightglue

LightGlue

Python

LightGlue: Local Feature Matching at Light Speed (ICCV 2023)

cvg

2024-01-13

2023-06-25

https://avatars.githubusercontent.com/u/840224?v=4

LightGlue: Local Feature Matching at Light Speed (ICCV 2023)

['deep-learning', 'image-matching', 'pose-estimation', 'transformers']

['deep-learning', 'image-matching', 'pose-estimation', 'transformers']

2023-11-21

[('facebookresearch/detr', 0.5226452350616455, 'ml-dl', 0)]

perf

https://github.com/airtai/faststream

[]

[]

[]

airtai/faststream

faststream

Python

https://faststream.airt.ai/latest/

FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.

airtai

2024-01-13

2022-12-01

https://avatars.githubusercontent.com/u/84014356?v=4

FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.

['asyncapi', 'asyncio', 'distributed-systems', 'fastkafka', 'faststream', 'kafka', 'nats', 'propan', 'rabbitmq', 'redis', 'stream-processing']

['asyncapi', 'asyncio', 'distributed-systems', 'fastkafka', 'faststream', 'kafka', 'nats', 'propan', 'rabbitmq', 'redis', 'stream-processing']

2024-01-13

[('pathwaycom/pathway', 0.6327610611915588, 'data', 1), ('samuelcolvin/arq', 0.6273122429847717, 'data', 2), ('python-trio/trio', 0.6224059462547302, 'perf', 0), ('magicstack/uvloop', 0.6053869128227234, 'util', 1), ('agronholm/anyio', 0.5833213329315186, 'perf', 1), ('miguelgrinberg/python-socketio', 0.5774848461151123, 'util', 1), ('pallets/quart', 0.5664022564888, 'web', 1), ('bogdanp/dramatiq', 0.5551525354385376, 'util', 1), ('aio-libs/aiohttp', 0.5545108914375305, 'web', 1), ('encode/httpx', 0.5473020672798157, 'web', 1), ('alirn76/panther', 0.5416358709335327, 'web', 0), ('sumerc/yappi', 0.5390495657920837, 'profiling', 1), ('backtick-se/cowait', 0.5363417267799377, 'util', 0), ('samuelcolvin/watchfiles', 0.5354728698730469, 'util', 1), ('encode/starlette', 0.5320852398872375, 'web', 0), ('fastai/fastcore', 0.5284400582313538, 'util', 0), ('neoteroi/blacksheep', 0.5211659073829651, 'web', 1), ('geeogi/async-python-lambda-template', 0.5201819539070129, 'template', 0), ('tornadoweb/tornado', 0.5190186500549316, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5075085759162903, 'template', 0), ('fugue-project/fugue', 0.5063053369522095, 'pandas', 1), ('mher/flower', 0.5014379620552063, 'perf', 2)]

study

https://github.com/realpython/python-guide

[]

[]

[]

realpython/python-guide

python-guide

Batchfile

https://docs.python-guide.org

Python best practices guidebook, written for humans.

realpython

2024-01-13

2011-03-15

https://avatars.githubusercontent.com/u/5448020?v=4

Python best practices guidebook, written for humans.

['book', 'guide', 'kennethreitz']

['book', 'guide', 'kennethreitz']

2023-06-13

[('amaargiru/pyroad', 0.6154986023902893, 'study', 0), ('wesm/pydata-book', 0.5613322854042053, 'study', 0), ('eleutherai/pyfra', 0.5539833307266235, 'ml', 0), ('brandon-rhodes/python-patterns', 0.5476312637329102, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5322774648666382, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5209663510322571, 'study', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5066047310829163, 'study', 0), ('python/cpython', 0.5037524104118347, 'util', 0), ('pytoolz/toolz', 0.5026092529296875, 'util', 0), ('ageron/handson-ml2', 0.5025880336761475, 'ml', 0)]

ml-dl

https://github.com/matterport/mask_rcnn

[]

[]

[]

matterport/mask_rcnn

Mask_RCNN

Python

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

matterport

2024-01-14

2017-10-19

https://avatars.githubusercontent.com/u/4206481?v=4

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

['instance-segmentation', 'keras', 'mask-rcnn', 'object-detection', 'tensorflow']

['instance-segmentation', 'keras', 'mask-rcnn', 'object-detection', 'tensorflow']

2019-03-31

[('open-mmlab/mmdetection', 0.5989094376564026, 'ml', 3), ('roboflow/notebooks', 0.5560668706893921, 'study', 1), ('nyandwi/modernconvnets', 0.5529733300209045, 'ml-dl', 2), ('blakeblackshear/frigate', 0.5429755449295044, 'util', 2), ('facebookresearch/segment-anything', 0.5424768328666687, 'ml-dl', 2), ('deci-ai/super-gradients', 0.5377986431121826, 'ml-dl', 1), ('facebookresearch/detectron', 0.5304756760597229, 'ml-dl', 0), ('roboflow/supervision', 0.528638482093811, 'ml', 3), ('nvlabs/gcvit', 0.5284178256988525, 'diffusion', 1), ('facebookresearch/detr', 0.5123329758644104, 'ml-dl', 0)]

util

https://github.com/keon/algorithms

[]

[]

[]

keon/algorithms

algorithms

Python

Minimal examples of data structures and algorithms in Python

keon

2024-01-13

2016-11-17

Minimal examples of data structures and algorithms in Python

['algorithm', 'algorithms', 'competitive-programming', 'data-structure', 'graph', 'search', 'sort', 'tree']

['algorithm', 'algorithms', 'competitive-programming', 'data-structure', 'graph', 'search', 'sort', 'tree']

2023-04-04

[('thealgorithms/python', 0.707886815071106, 'study', 1), ('joowani/binarytree', 0.6219033002853394, 'util', 2), ('python-odin/odin', 0.6064568161964417, 'util', 0), ('pandas-dev/pandas', 0.6043705940246582, 'pandas', 0), ('pyomo/pyomo', 0.5572477579116821, 'math', 0), ('krzjoa/awesome-python-data-science', 0.5512800812721252, 'study', 0), ('gbeced/pyalgotrade', 0.5470435619354248, 'finance', 0), ('quantopian/zipline', 0.5438115000724792, 'finance', 0), ('networkx/networkx', 0.543645977973938, 'graph', 0), ('atsushisakai/pythonrobotics', 0.5353425741195679, 'sim', 1), ('sympy/sympy', 0.5286232233047485, 'math', 0), ('pytoolz/toolz', 0.520076334476471, 'util', 0), ('quantconnect/lean', 0.512477457523346, 'finance', 1), ('tiangolo/sqlmodel', 0.5121207237243652, 'data', 0), ('ranaroussi/quantstats', 0.5114973187446594, 'finance', 0), ('dagworks-inc/hamilton', 0.5108981728553772, 'ml-ops', 0), ('plotly/dash', 0.5095182657241821, 'viz', 0), ('scikit-learn/scikit-learn', 0.5060738921165466, 'ml', 0), ('rasbt/mlxtend', 0.5041128993034363, 'ml', 0), ('python/cpython', 0.5038732290267944, 'util', 0), ('scikit-mobility/scikit-mobility', 0.5026717782020569, 'gis', 0)]

nlp

https://github.com/rare-technologies/gensim

[]

[]

[]

rare-technologies/gensim

gensim

Python

https://radimrehurek.com/gensim

Topic Modelling for Humans

rare-technologies

2024-01-14

2011-02-10

Topic Modelling for Humans

['data-mining', 'data-science', 'document-similarity', 'fasttext', 'gensim', 'information-retrieval', 'machine-learning', 'natural-language-processing', 'neural-network', 'nlp', 'topic-modeling', 'word-embeddings', 'word-similarity', 'word2vec']

['data-mining', 'data-science', 'document-similarity', 'fasttext', 'gensim', 'information-retrieval', 'machine-learning', 'natural-language-processing', 'neural-network', 'nlp', 'topic-modeling', 'word-embeddings', 'word-similarity', 'word2vec']

2023-10-01

[('ddangelov/top2vec', 0.59908527135849, 'nlp', 2), ('maartengr/bertopic', 0.5905485153198242, 'nlp', 3), ('brettkromkamp/topic-db', 0.539949893951416, 'data', 0), ('ddbourgin/numpy-ml', 0.5375690460205078, 'ml', 3), ('sebischair/lbl2vec', 0.5351875424385071, 'nlp', 4), ('sloria/textblob', 0.5176156759262085, 'nlp', 2), ('milvus-io/bootcamp', 0.5120472311973572, 'data', 1)]

util

https://github.com/ninja-build/ninja

['build']

Ninja is a small build system with a focus on speed.

[]

[]

ninja-build/ninja

ninja

C++

https://ninja-build.org/

a small build system with a focus on speed

ninja-build

2024-01-14

2011-02-06

https://avatars.githubusercontent.com/u/11653218?v=4

a small build system with a focus on speed

[]

['build']

2024-01-02

[('scikit-build/scikit-build', 0.562438428401947, 'ml', 0)]

ml

https://github.com/epistasislab/tpot

[]

[]

[]

epistasislab/tpot

tpot

Python

http://epistasislab.github.io/tpot/

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

epistasislab

2024-01-13

2015-11-03

https://avatars.githubusercontent.com/u/20861190?v=4

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

['adsp', 'ag066833', 'aiml', 'alzheimer', 'alzheimers', 'automated-machine-learning', 'automation', 'automl', 'data-science', 'feature-engineering', 'gradient-boosting', 'hyperparameter-optimization', 'machine-learning', 'model-selection', 'nia', 'parameter-tuning', 'random-forest', 'scikit-learn', 'u01ag066833']

['adsp', 'ag066833', 'aiml', 'alzheimer', 'alzheimers', 'automated-machine-learning', 'automation', 'automl', 'data-science', 'feature-engineering', 'gradient-boosting', 'hyperparameter-optimization', 'machine-learning', 'model-selection', 'nia', 'parameter-tuning', 'random-forest', 'scikit-learn', 'u01ag066833']

2023-12-08

[('automl/auto-sklearn', 0.6574358940124512, 'ml', 4), ('featurelabs/featuretools', 0.6507097482681274, 'ml', 6), ('microsoft/nni', 0.6485167741775513, 'ml', 6), ('google/pyglove', 0.6232022643089294, 'util', 2), ('scikit-learn/scikit-learn', 0.6184797286987305, 'ml', 2), ('mljar/mljar-supervised', 0.6145864129066467, 'ml', 8), ('google/vizier', 0.6137571930885315, 'ml', 2), ('microsoft/flaml', 0.6104899048805237, 'ml', 7), ('nccr-itmo/fedot', 0.6070546507835388, 'ml-ops', 6), ('gradio-app/gradio', 0.5918328166007996, 'viz', 2), ('rasbt/mlxtend', 0.5866554379463196, 'ml', 2), ('determined-ai/determined', 0.5670955777168274, 'ml-ops', 3), ('districtdatalabs/yellowbrick', 0.5539801120758057, 'ml', 3), ('ray-project/ray', 0.5462473034858704, 'ml-ops', 5), ('wandb/client', 0.5462185144424438, 'ml', 3), ('scikit-learn-contrib/imbalanced-learn', 0.545037567615509, 'ml', 2), ('merantix-momentum/squirrel-core', 0.5355274081230164, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5343623161315918, 'ml-interpretability', 0), ('pycaret/pycaret', 0.5342784523963928, 'ml', 2), ('awslabs/autogluon', 0.5319231748580933, 'ml', 6), ('scikit-optimize/scikit-optimize', 0.5264012217521667, 'ml', 3), ('kubeflow/fairing', 0.5261349081993103, 'ml-ops', 0), ('ml-tooling/opyrator', 0.5252538323402405, 'viz', 1), ('ageron/handson-ml2', 0.5202245712280273, 'ml', 0), ('dagworks-inc/hamilton', 0.5198063850402832, 'ml-ops', 3), ('catboost/catboost', 0.515169084072113, 'ml', 3), ('polyaxon/polyaxon', 0.5143507122993469, 'ml-ops', 3), ('keras-team/autokeras', 0.5138368010520935, 'ml-dl', 3), ('koaning/scikit-lego', 0.5125021934509277, 'ml', 2), ('rasbt/machine-learning-book', 0.5111925601959229, 'study', 2), ('online-ml/river', 0.5093849897384644, 'ml', 2), ('karpathy/micrograd', 0.5062547922134399, 'study', 0), ('huggingface/datasets', 0.50594562292099, 'nlp', 1), ('rafiqhasan/auto-tensorflow', 0.5045480132102966, 'ml-dl', 2), ('firmai/atspy', 0.5008826851844788, 'time-series', 0)]

web

https://github.com/falconry/falcon

[]

[]

[]

falconry/falcon

falcon

Python

https://falcon.readthedocs.io/en/stable/

The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.

falconry

2024-01-12

2012-12-06

https://avatars.githubusercontent.com/u/11353642?v=4

The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.

['api', 'api-rest', 'asgi', 'framework', 'http', 'microservices', 'rest', 'web', 'wsgi']

['api', 'api-rest', 'asgi', 'framework', 'http', 'microservices', 'rest', 'web', 'wsgi']

2023-12-26

[('pallets/flask', 0.7083405256271362, 'web', 1), ('neoteroi/blacksheep', 0.6875892877578735, 'web', 4), ('pallets/quart', 0.6842796206474304, 'web', 1), ('starlite-api/starlite', 0.6774393916130066, 'web', 3), ('bottlepy/bottle', 0.677134096622467, 'web', 2), ('encode/uvicorn', 0.6588360667228699, 'web', 2), ('klen/muffin', 0.6537138819694519, 'web', 1), ('python-restx/flask-restx', 0.6462195515632629, 'web', 2), ('masoniteframework/masonite', 0.6416914463043213, 'web', 2), ('pallets/werkzeug', 0.630772590637207, 'web', 2), ('simple-salesforce/simple-salesforce', 0.6300815939903259, 'data', 1), ('hugapi/hug', 0.6300604343414307, 'util', 1), ('requests/toolbelt', 0.6299983263015747, 'util', 1), ('pylons/pyramid', 0.6222757697105408, 'web', 1), ('webpy/webpy', 0.6171799302101135, 'web', 0), ('encode/httpx', 0.6106772422790527, 'web', 1), ('vitalik/django-ninja', 0.6103278994560242, 'web', 0), ('tiangolo/fastapi', 0.608084499835968, 'web', 4), ('cherrypy/cherrypy', 0.6051623821258545, 'web', 1), ('reflex-dev/reflex', 0.5998751521110535, 'web', 1), ('scrapy/scrapy', 0.5992222428321838, 'data', 1), ('tiangolo/sqlmodel', 0.5907256007194519, 'data', 0), ('eleutherai/pyfra', 0.5904573202133179, 'ml', 0), ('nficano/python-lambda', 0.5897996425628662, 'util', 1), ('jordaneremieff/mangum', 0.5848598480224609, 'web', 1), ('aws/chalice', 0.5833958387374878, 'web', 0), ('alirn76/panther', 0.5779109001159668, 'web', 1), ('pyeve/eve', 0.5698334574699402, 'web', 1), ('ml-tooling/opyrator', 0.5693379044532776, 'viz', 1), ('willmcgugan/textual', 0.562833845615387, 'term', 1), ('psf/requests', 0.5613847970962524, 'web', 1), ('pylons/waitress', 0.5612348318099976, 'web', 0), ('timofurrer/awesome-asyncio', 0.5593904852867126, 'study', 0), ('backtick-se/cowait', 0.5538949370384216, 'util', 0), ('taverntesting/tavern', 0.5523936152458191, 'testing', 1), ('plotly/dash', 0.5521128177642822, 'viz', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5512214303016663, 'template', 0), ('flet-dev/flet', 0.5503961443901062, 'web', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5487000942230225, 'template', 0), ('nasdaq/data-link-python', 0.5472946763038635, 'finance', 0), ('kubeflow/fairing', 0.5438461303710938, 'ml-ops', 0), ('fastai/fastcore', 0.5430771708488464, 'util', 0), ('holoviz/panel', 0.5421604514122009, 'viz', 0), ('ethereum/web3.py', 0.540778636932373, 'crypto', 0), ('clips/pattern', 0.5375832915306091, 'nlp', 0), ('ets-labs/python-dependency-injector', 0.5372539758682251, 'util', 0), ('amzn/ion-python', 0.5367324352264404, 'data', 0), ('roniemartinez/dude', 0.5347031354904175, 'util', 1), ('huge-success/sanic', 0.5344210267066956, 'web', 3), ('benoitc/gunicorn', 0.5321671366691589, 'web', 2), ('python-odin/odin', 0.5320534706115723, 'util', 0), ('merantix-momentum/squirrel-core', 0.5313084721565247, 'ml', 0), ('ibis-project/ibis', 0.5278680324554443, 'data', 0), ('locustio/locust', 0.5264105200767517, 'testing', 1), ('pyinfra-dev/pyinfra', 0.5231207609176636, 'util', 0), ('openai/openai-python', 0.5228433609008789, 'util', 0), ('ajndkr/lanarky', 0.5218005180358887, 'llm', 1), ('dylanhogg/awesome-python', 0.5216991901397705, 'study', 0), ('simonw/datasette', 0.5216467380523682, 'data', 1), ('pytoolz/toolz', 0.5214901566505432, 'util', 0), ('lk-geimfari/mimesis', 0.5205872654914856, 'data', 0), ('geopandas/geopandas', 0.5166769027709961, 'gis', 0), ('alirezamika/autoscraper', 0.513289749622345, 'data', 0), ('aio-libs/aiohttp', 0.512401819229126, 'web', 1), ('pytables/pytables', 0.5114437937736511, 'data', 0), ('pynamodb/pynamodb', 0.5108199119567871, 'data', 0), ('sqlalchemy/sqlalchemy', 0.509283185005188, 'data', 0), ('radiantearth/radiant-mlhub', 0.5079211592674255, 'gis', 0), ('snyk-labs/pysnyk', 0.5076401829719543, 'security', 1), ('1200wd/bitcoinlib', 0.5075222849845886, 'crypto', 0), ('amaargiru/pyroad', 0.5072835087776184, 'study', 0), ('micropython/micropython', 0.507040798664093, 'util', 0), ('eventual-inc/daft', 0.5012557506561279, 'pandas', 0), ('shishirpatil/gorilla', 0.500187873840332, 'llm', 1)]

util

https://github.com/paramiko/paramiko

[]

[]

[]

paramiko/paramiko

paramiko

Python

http://paramiko.org

The leading native Python SSHv2 protocol library.

paramiko

2024-01-14

2009-02-02

https://avatars.githubusercontent.com/u/1108455?v=4

The leading native Python SSHv2 protocol library.

[]

[]

2023-12-18

[('pypy/pypy', 0.6160950064659119, 'util', 0), ('pyston/pyston', 0.5798661708831787, 'util', 0), ('secdev/scapy', 0.5442431569099426, 'util', 0), ('legrandin/pycryptodome', 0.5410947203636169, 'util', 0), ('pytoolz/toolz', 0.5407304763793945, 'util', 0), ('1200wd/bitcoinlib', 0.5394938588142395, 'crypto', 0), ('ethereum/py-evm', 0.5358531475067139, 'crypto', 0), ('urwid/urwid', 0.5337045192718506, 'term', 0), ('cherrypy/cherrypy', 0.5295555591583252, 'web', 0), ('oracle/graalpython', 0.5263757109642029, 'util', 0), ('encode/httpx', 0.5251051187515259, 'web', 0), ('websocket-client/websocket-client', 0.5153641700744629, 'web', 0), ('pyca/cryptography', 0.5152232646942139, 'util', 0), ('python/cpython', 0.5128664374351501, 'util', 0), ('pyca/pynacl', 0.5029951930046082, 'util', 0), ('libtcod/python-tcod', 0.502030611038208, 'gamedev', 0), ('primal100/pybitcointools', 0.5015236139297485, 'crypto', 0)]

ml-dl

https://github.com/nvidia/apex

[]

[]

[]

nvidia/apex

apex

Python

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

nvidia

2024-01-14

2018-04-23

https://avatars.githubusercontent.com/u/1728152?v=4

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

[]

[]

2024-01-12

[('pytorch/ignite', 0.76711106300354, 'ml-dl', 0), ('huggingface/accelerate', 0.7648141980171204, 'ml', 0), ('intel/intel-extension-for-pytorch', 0.7110769152641296, 'perf', 0), ('skorch-dev/skorch', 0.6882312893867493, 'ml-dl', 0), ('pytorch/data', 0.6665452122688293, 'data', 0), ('laekov/fastmoe', 0.6603500247001648, 'ml', 0), ('mrdbourke/pytorch-deep-learning', 0.6514618396759033, 'study', 0), ('karpathy/micrograd', 0.6441670060157776, 'study', 0), ('rasbt/machine-learning-book', 0.6338503956794739, 'study', 0), ('arogozhnikov/einops', 0.6182481646537781, 'ml-dl', 0), ('denys88/rl_games', 0.6122349500656128, 'ml-rl', 0), ('karpathy/mingpt', 0.6042015552520752, 'llm', 0), ('rentruewang/koila', 0.6033901572227478, 'ml', 0), ('nicolas-chaulet/torch-points3d', 0.5915487408638, 'ml', 0), ('horovod/horovod', 0.5866835117340088, 'ml-ops', 0), ('allenai/allennlp', 0.5706870555877686, 'nlp', 0), ('pyg-team/pytorch_geometric', 0.5672152042388916, 'ml-dl', 0), ('pytorch/botorch', 0.5640817880630493, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5612041354179382, 'ml-dl', 0), ('determined-ai/determined', 0.5580594539642334, 'ml-ops', 0), ('pytorch/captum', 0.5534337759017944, 'ml-interpretability', 0), ('ashleve/lightning-hydra-template', 0.553119957447052, 'util', 0), ('davidmrau/mixture-of-experts', 0.5525276064872742, 'ml', 0), ('pytorch/rl', 0.552091121673584, 'ml-rl', 0), ('huggingface/transformers', 0.5461992621421814, 'nlp', 0), ('kshitij12345/torchnnprofiler', 0.5381832718849182, 'profiling', 0), ('blackhc/toma', 0.5342817902565002, 'ml-dl', 0), ('pytorch-labs/gpt-fast', 0.5303529500961304, 'llm', 0), ('xl0/lovely-tensors', 0.5290429592132568, 'ml-dl', 0), ('facebookresearch/pytorch3d', 0.5290184020996094, 'ml-dl', 0), ('intellabs/bayesian-torch', 0.5274893045425415, 'ml', 0), ('thu-ml/tianshou', 0.5245383381843567, 'ml-rl', 0), ('timdettmers/bitsandbytes', 0.5227283239364624, 'util', 0), ('faster-cpython/tools', 0.5224118828773499, 'perf', 0), ('salesforce/blip', 0.5196071267127991, 'diffusion', 0), ('mcahny/deep-video-inpainting', 0.514928936958313, 'ml-dl', 0), ('huggingface/optimum', 0.5144795179367065, 'ml', 0), ('huggingface/peft', 0.5134326219558716, 'llm', 0), ('uber/petastorm', 0.5052030086517334, 'data', 0), ('dask/dask-ml', 0.5042513608932495, 'ml', 0), ('paddlepaddle/paddle', 0.5022732019424438, 'ml-dl', 0), ('hazyresearch/hgcn', 0.502190113067627, 'ml', 0)]

data

https://github.com/yzhao062/pyod

[]

[]

[]

yzhao062/pyod

pyod

Python

http://pyod.readthedocs.io

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

yzhao062

2024-01-13

2017-10-03

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

['anomaly', 'anomaly-detection', 'autoencoder', 'data-analysis', 'data-mining', 'data-science', 'deep-learning', 'fraud-detection', 'machine-learning', 'neural-networks', 'novelty-detection', 'out-of-distribution-detection', 'outlier-detection', 'outlier-ensembles', 'outliers', 'unsupervised-learning']

['anomaly', 'anomaly-detection', 'autoencoder', 'data-analysis', 'data-mining', 'data-science', 'deep-learning', 'fraud-detection', 'machine-learning', 'neural-networks', 'novelty-detection', 'out-of-distribution-detection', 'outlier-detection', 'outlier-ensembles', 'outliers', 'unsupervised-learning']

2023-12-16

[('pycaret/pycaret', 0.7633078694343567, 'ml', 3), ('unit8co/darts', 0.7557379603385925, 'time-series', 4), ('aistream-peelout/flow-forecast', 0.6631090044975281, 'time-series', 2), ('tdameritrade/stumpy', 0.6203436255455017, 'time-series', 2), ('rasbt/mlxtend', 0.6038527488708496, 'ml', 4), ('scikit-learn-contrib/imbalanced-learn', 0.589371383190155, 'ml', 3), ('salesforce/merlion', 0.5439088940620422, 'time-series', 2), ('featurelabs/featuretools', 0.5403005480766296, 'ml', 2), ('salesforce/logai', 0.5268727540969849, 'util', 2), ('mdbloice/augmentor', 0.5144423842430115, 'ml', 3), ('scikit-learn/scikit-learn', 0.5111293196678162, 'ml', 3), ('alkaline-ml/pmdarima', 0.5062800049781799, 'time-series', 1), ('jeshraghian/snntorch', 0.5056750178337097, 'ml-dl', 2)]

llm

https://github.com/openlm-research/open_llama

['llama', 'language-model']

OpenLLaMA: An Open Reproduction of LLaMA

['2302.13971']

[]

openlm-research/open_llama

open_llama

OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset

openlm-research

2024-01-13

2023-04-28

https://avatars.githubusercontent.com/u/132110378?v=4

OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset

[]

['language-model', 'llama']

2023-07-16

[('microsoft/llama-2-onnx', 0.6107590794563293, 'llm', 2), ('jzhang38/tinyllama', 0.5819482207298279, 'llm', 2), ('facebookresearch/llama', 0.5816770195960999, 'llm', 2), ('togethercomputer/redpajama-data', 0.5621833801269531, 'llm', 0), ('lm-sys/fastchat', 0.5599814653396606, 'llm', 1), ('karpathy/llama2.c', 0.5405749678611755, 'llm', 2), ('cg123/mergekit', 0.537260890007019, 'llm', 1), ('facebookresearch/codellama', 0.5370882153511047, 'llm', 2), ('facebookresearch/llama-recipes', 0.536719799041748, 'llm', 2), ('yueyu1030/attrprompt', 0.5280580520629883, 'llm', 0), ('bobazooba/xllm', 0.5272755026817322, 'llm', 1), ('lightning-ai/lit-llama', 0.5196517109870911, 'llm', 2), ('bigscience-workshop/petals', 0.5177329778671265, 'data', 1), ('lupantech/chameleon-llm', 0.5159003138542175, 'llm', 1), ('mshumer/gpt-llm-trainer', 0.5152719616889954, 'llm', 0), ('eleutherai/the-pile', 0.5138996839523315, 'data', 0), ('juncongmoo/pyllama', 0.5135765671730042, 'llm', 0), ('google-research/language', 0.5121092796325684, 'nlp', 0), ('tairov/llama2.mojo', 0.5117022395133972, 'llm', 1), ('tigerlab-ai/tiger', 0.5050448179244995, 'llm', 0), ('run-llama/llama-lab', 0.5049968957901001, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5018919110298157, 'llm', 1), ('aiwaves-cn/agents', 0.5005034804344177, 'nlp', 1)]

gamedev

https://github.com/pygame/pygame

[]

[]

[]

pygame/pygame

pygame

C

https://www.pygame.org

🐍🎮 pygame (the library) is a Free and Open Source python programming language library for making multimedia applications like games built on top of the excellent SDL library. C, Python, Native, OpenGL.

pygame

2024-01-14

2017-03-26

https://avatars.githubusercontent.com/u/20628127?v=4

🐍🎮 pygame (the library) is a Free and Open Source python programming language library for making multimedia applications like games built on top of the excellent SDL library. C, Python, Native, OpenGL.

['game-dev', 'game-development', 'gamedev', 'pygame', 'sdl', 'sdl2']

['game-dev', 'game-development', 'gamedev', 'pygame', 'sdl', 'sdl2']

2023-12-30

[('pyglet/pyglet', 0.717469334602356, 'gamedev', 1), ('renpy/pygame_sdl2', 0.7130681872367859, 'gamedev', 2), ('lordmauve/pgzero', 0.6985493302345276, 'gamedev', 1), ('pygamelib/pygamelib', 0.6512402892112732, 'gamedev', 2), ('pythonarcade/arcade', 0.6259638071060181, 'gamedev', 0), ('panda3d/panda3d', 0.609826922416687, 'gamedev', 2), ('kitao/pyxel', 0.5883877873420715, 'gamedev', 2), ('pokepetter/ursina', 0.5800055861473083, 'gamedev', 1), ('viblo/pymunk', 0.5696349740028381, 'sim', 1), ('renpy/renpy', 0.5524816513061523, 'viz', 0), ('pypy/pypy', 0.5523542165756226, 'util', 0), ('pytoolz/toolz', 0.5287861824035645, 'util', 0), ('hoffstadt/dearpygui', 0.5225834846496582, 'gui', 0), ('urwid/urwid', 0.5163580775260925, 'term', 0), ('jquast/blessed', 0.504092812538147, 'term', 0)]

gis

https://github.com/python-visualization/folium

[]

[]

[]

python-visualization/folium

folium

Python

https://python-visualization.github.io/folium/

Python Data. Leaflet.js Maps.

python-visualization

2024-01-13

2013-05-09

https://avatars.githubusercontent.com/u/9969242?v=4

Python Data. Leaflet.js Maps.

['data-science', 'data-visualization', 'javascript', 'maps']

['data-science', 'data-visualization', 'javascript', 'maps']

2024-01-02

[('jupyter-widgets/ipyleaflet', 0.6334434151649475, 'gis', 0), ('bokeh/bokeh', 0.5900196433067322, 'viz', 1), ('plotly/dash', 0.5898042321205139, 'viz', 2), ('giswqs/mapwidget', 0.5697619915008545, 'gis', 0), ('giswqs/geemap', 0.5445337295532227, 'gis', 1), ('raphaelquast/eomaps', 0.5424359440803528, 'gis', 0), ('opengeos/leafmap', 0.5398542284965515, 'gis', 1), ('holoviz/panel', 0.5396947860717773, 'viz', 0), ('plotly/plotly.py', 0.5229291319847107, 'viz', 0), ('man-group/dtale', 0.5144950747489929, 'viz', 2)]

web

https://github.com/pallets/werkzeug

[]

[]

[]

pallets/werkzeug

werkzeug

Python

https://werkzeug.palletsprojects.com

The comprehensive WSGI web application library.

pallets

2024-01-13

2010-10-18

https://avatars.githubusercontent.com/u/16748505?v=4

The comprehensive WSGI web application library.

['http', 'pallets', 'werkzeug', 'wsgi']

['http', 'pallets', 'werkzeug', 'wsgi']

2024-01-01

[('pallets/flask', 0.7842201590538025, 'web', 3), ('pylons/pyramid', 0.7471210360527039, 'web', 1), ('bottlepy/bottle', 0.6876417994499207, 'web', 1), ('benoitc/gunicorn', 0.6659462451934814, 'web', 2), ('masoniteframework/masonite', 0.6412118673324585, 'web', 0), ('falconry/falcon', 0.630772590637207, 'web', 2), ('pylons/waitress', 0.6237661838531494, 'web', 0), ('pylons/webob', 0.6051927804946899, 'web', 1), ('neoteroi/blacksheep', 0.5996673703193665, 'web', 1), ('webpy/webpy', 0.5957249999046326, 'web', 0), ('cherrypy/cherrypy', 0.5869470834732056, 'web', 1), ('encode/uvicorn', 0.5832026600837708, 'web', 1), ('klen/muffin', 0.5703849196434021, 'web', 0), ('encode/httpx', 0.5651618838310242, 'web', 1), ('scrapy/scrapy', 0.5650268197059631, 'data', 0), ('reflex-dev/reflex', 0.5643238425254822, 'web', 0), ('requests/toolbelt', 0.5638414025306702, 'util', 1), ('psf/requests', 0.559667706489563, 'web', 1), ('pallets/quart', 0.5403130054473877, 'web', 0), ('python-restx/flask-restx', 0.5365567803382874, 'web', 0), ('willmcgugan/textual', 0.5303859114646912, 'term', 0), ('hugapi/hug', 0.5272501111030579, 'util', 1), ('eleutherai/pyfra', 0.523544430732727, 'ml', 0), ('emmett-framework/emmett', 0.5205287337303162, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.520187497138977, 'template', 0), ('mlc-ai/web-llm', 0.5196402668952942, 'llm', 0), ('roniemartinez/dude', 0.5181063413619995, 'util', 0), ('flet-dev/flet', 0.5143693089485168, 'web', 0), ('starlite-api/starlite', 0.5068367123603821, 'web', 0), ('aminalaee/sqladmin', 0.5044029355049133, 'data', 1), ('encode/starlette', 0.5027870535850525, 'web', 1), ('clips/pattern', 0.5018793344497681, 'nlp', 0)]

llm

https://github.com/nat/openplayground

['language-model', 'local']

[]

[]

nat/openplayground

openplayground

TypeScript

An LLM playground you can run on your laptop

nat

2024-01-14

2023-02-26

An LLM playground you can run on your laptop

[]

['language-model', 'local']

2023-06-05

[('eugeneyan/open-llms', 0.6209505796432495, 'study', 0), ('alphasecio/langchain-examples', 0.6207661628723145, 'llm', 0), ('hwchase17/langchain', 0.607382595539093, 'llm', 1), ('young-geng/easylm', 0.593249499797821, 'llm', 1), ('thudm/chatglm2-6b', 0.5894226431846619, 'llm', 0), ('mlc-ai/web-llm', 0.589094340801239, 'llm', 1), ('nomic-ai/gpt4all', 0.5870195627212524, 'llm', 1), ('langchain-ai/langgraph', 0.5708506107330322, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5689014792442322, 'llm', 0), ('tigerlab-ai/tiger', 0.5651535987854004, 'llm', 0), ('salesforce/xgen', 0.5570892095565796, 'llm', 1), ('hiyouga/llama-factory', 0.5530926585197449, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5530924797058105, 'llm', 1), ('agenta-ai/agenta', 0.5450541973114014, 'llm', 0), ('salesforce/codet5', 0.5432443022727966, 'nlp', 1), ('microsoft/llama-2-onnx', 0.5403831005096436, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5346694588661194, 'perf', 0), ('microsoft/torchscale', 0.5345559120178223, 'llm', 0), ('conceptofmind/toolformer', 0.5319477915763855, 'llm', 1), ('langchain-ai/langsmith-cookbook', 0.5300737619400024, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5199169516563416, 'study', 0), ('nvidia/nemo-guardrails', 0.5180691480636597, 'llm', 1), ('ctlllll/llm-toolmaker', 0.5115349292755127, 'llm', 1), ('sjtu-ipads/powerinfer', 0.5090302228927612, 'llm', 0), ('hannibal046/awesome-llm', 0.5065643787384033, 'study', 1), ('deep-diver/pingpong', 0.5059070587158203, 'llm', 0), ('prefecthq/langchain-prefect', 0.5041869282722473, 'llm', 0), ('mlc-ai/mlc-llm', 0.5039643049240112, 'llm', 1), ('cg123/mergekit', 0.5010144710540771, 'llm', 0)]

security

https://github.com/pycqa/bandit

['code-quality']

[]

[]

pycqa/bandit

bandit

Python

https://bandit.readthedocs.io

Bandit is a tool designed to find common security issues in Python code.

pycqa

2024-01-13

2018-04-26

https://avatars.githubusercontent.com/u/8749848?v=4

Bandit is a tool designed to find common security issues in Python code.

['bandit', 'linter', 'security', 'security-scanner', 'security-tools', 'static-code-analysis']

['bandit', 'code-quality', 'linter', 'security', 'security-scanner', 'security-tools', 'static-code-analysis']

2024-01-13

[('aswinnnn/pyscan', 0.5267046093940735, 'security', 3), ('nedbat/coveragepy', 0.5142317414283752, 'testing', 0)]

util

https://github.com/icloud-photos-downloader/icloud_photos_downloader

['photos-export', 'library-photos']

[]

[]

icloud-photos-downloader/icloud_photos_downloader

icloud_photos_downloader

Python

A command-line tool to download photos from iCloud

icloud-photos-downloader

2024-01-14

2016-05-13

https://avatars.githubusercontent.com/u/73247967?v=4

A command-line tool to download photos from iCloud

[]

['library-photos', 'photos-export']

2024-01-05

[]

util

https://github.com/agronholm/apscheduler

[]

[]

[]

agronholm/apscheduler

apscheduler

Python

Task scheduling library for Python

agronholm

2024-01-14

2016-03-27

Task scheduling library for Python

[]

[]

2024-01-11

[('dbader/schedule', 0.7123571634292603, 'util', 0), ('dask/dask', 0.6700900197029114, 'perf', 0), ('pyinvoke/invoke', 0.6340285539627075, 'util', 0), ('pypy/pypy', 0.6140989065170288, 'util', 0), ('pytoolz/toolz', 0.6112861037254333, 'util', 0), ('bogdanp/dramatiq', 0.6053295135498047, 'util', 0), ('dask/distributed', 0.5948215126991272, 'perf', 0), ('python/cpython', 0.5778864622116089, 'util', 0), ('joblib/loky', 0.5760906934738159, 'perf', 0), ('eleutherai/pyfra', 0.5697214603424072, 'ml', 0), ('faster-cpython/ideas', 0.5676429867744446, 'perf', 0), ('erotemic/ubelt', 0.5611792802810669, 'util', 0), ('pyston/pyston', 0.5583381652832031, 'util', 0), ('pympler/pympler', 0.5432737469673157, 'perf', 0), ('joblib/joblib', 0.5400955080986023, 'util', 0), ('micropython/micropython', 0.5366904139518738, 'util', 0), ('sumerc/yappi', 0.5361764430999756, 'profiling', 0), ('ipython/ipyparallel', 0.529596745967865, 'perf', 0), ('fastai/fastcore', 0.5230339765548706, 'util', 0), ('kubeflow/fairing', 0.5141321420669556, 'ml-ops', 0), ('requests/toolbelt', 0.5136269330978394, 'util', 0), ('samuelcolvin/arq', 0.5115315914154053, 'data', 0), ('python-trio/trio', 0.5111809968948364, 'perf', 0), ('firmai/atspy', 0.5056720972061157, 'time-series', 0), ('faster-cpython/tools', 0.5040667057037354, 'perf', 0), ('artemyk/dynpy', 0.5037860870361328, 'sim', 0), ('merantix-momentum/squirrel-core', 0.5034119486808777, 'ml', 0), ('backtick-se/cowait', 0.502835750579834, 'util', 0), ('urwid/urwid', 0.501419186592102, 'term', 0)]

gis

https://github.com/gboeing/osmnx

[]

[]

[]

gboeing/osmnx

osmnx

Python

https://osmnx.readthedocs.io

OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

gboeing

2024-01-13

2016-07-24

OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

['geography', 'geospatial', 'gis', 'mapping', 'networks', 'networkx', 'openstreetmap', 'osm', 'osmnx', 'overpass-api', 'routing', 'spatial', 'spatial-analysis', 'spatial-data', 'street-networks', 'transport', 'transportation', 'urban', 'urban-planning']

['geography', 'geospatial', 'gis', 'mapping', 'networks', 'networkx', 'openstreetmap', 'osm', 'osmnx', 'overpass-api', 'routing', 'spatial', 'spatial-analysis', 'spatial-data', 'street-networks', 'transport', 'transportation', 'urban', 'urban-planning']

2024-01-12

[('gboeing/osmnx-examples', 0.7930247187614441, 'gis', 5), ('marceloprates/prettymaps', 0.6797459125518799, 'viz', 1), ('gboeing/street-network-models', 0.5225948691368103, 'sim', 0), ('westhealth/pyvis', 0.5170513987541199, 'graph', 1)]

ml-dl

https://github.com/pytorch/ignite

[]

[]

[]

pytorch/ignite

ignite

Python

https://pytorch-ignite.ai

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

pytorch

2024-01-13

2017-11-23

https://avatars.githubusercontent.com/u/21003710?v=4

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

['deep-learning', 'machine-learning', 'metrics', 'neural-network', 'pytorch']

['deep-learning', 'machine-learning', 'metrics', 'neural-network', 'pytorch']

2024-01-11

[('skorch-dev/skorch', 0.8268391489982605, 'ml-dl', 2), ('mrdbourke/pytorch-deep-learning', 0.7811650037765503, 'study', 3), ('intel/intel-extension-for-pytorch', 0.7741976380348206, 'perf', 4), ('nvidia/apex', 0.76711106300354, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.7287918925285339, 'ml-dl', 2), ('rasbt/machine-learning-book', 0.7284553050994873, 'study', 3), ('karpathy/micrograd', 0.7160765528678894, 'study', 0), ('pytorch/captum', 0.6821672916412354, 'ml-interpretability', 0), ('denys88/rl_games', 0.6810092926025391, 'ml-rl', 2), ('pytorch/rl', 0.6654600501060486, 'ml-rl', 2), ('allenai/allennlp', 0.6625421047210693, 'nlp', 2), ('huggingface/accelerate', 0.6613282561302185, 'ml', 0), ('intellabs/bayesian-torch', 0.6580493450164795, 'ml', 2), ('pytorch/data', 0.655742347240448, 'data', 0), ('ashleve/lightning-hydra-template', 0.6497684717178345, 'util', 2), ('xl0/lovely-tensors', 0.6465728282928467, 'ml-dl', 2), ('arogozhnikov/einops', 0.6346755027770996, 'ml-dl', 2), ('huggingface/transformers', 0.6338894367218018, 'nlp', 3), ('lightly-ai/lightly', 0.6327634453773499, 'ml', 3), ('laekov/fastmoe', 0.6223480701446533, 'ml', 0), ('rentruewang/koila', 0.6201809644699097, 'ml', 4), ('lucidrains/imagen-pytorch', 0.6169648766517639, 'ml-dl', 1), ('nicolas-chaulet/torch-points3d', 0.6167212128639221, 'ml', 0), ('ageron/handson-ml2', 0.6152977347373962, 'ml', 0), ('hysts/pytorch_image_classification', 0.615060031414032, 'ml-dl', 1), ('facebookresearch/pytorch3d', 0.6142131686210632, 'ml-dl', 0), ('neuralmagic/sparseml', 0.6128144860267639, 'ml-dl', 1), ('thu-ml/tianshou', 0.6126653552055359, 'ml-rl', 1), ('determined-ai/determined', 0.6104621291160583, 'ml-ops', 3), ('horovod/horovod', 0.6100185513496399, 'ml-ops', 3), ('tensorlayer/tensorlayer', 0.6070582866668701, 'ml-rl', 2), ('oml-team/open-metric-learning', 0.6061093807220459, 'ml', 2), ('kshitij12345/torchnnprofiler', 0.6059595346450806, 'profiling', 0), ('ggerganov/ggml', 0.6042031645774841, 'ml', 1), ('keras-team/keras', 0.5964016318321228, 'ml-dl', 3), ('tensorflow/tensorflow', 0.5939213633537292, 'ml-dl', 3), ('tensorflow/lucid', 0.5938147902488708, 'ml-interpretability', 1), ('nvidia/deeplearningexamples', 0.5919349193572998, 'ml-dl', 2), ('mdbloice/augmentor', 0.5899521708488464, 'ml', 2), ('pytorch/torchrec', 0.5860687494277954, 'ml-dl', 2), ('uber/petastorm', 0.5855341553688049, 'data', 3), ('nvlabs/gcvit', 0.5840114951133728, 'diffusion', 1), ('salesforce/blip', 0.5812243223190308, 'diffusion', 0), ('lutzroeder/netron', 0.5811353325843811, 'ml', 4), ('tensorflow/tensor2tensor', 0.5802233219146729, 'ml', 2), ('rasbt/deeplearning-models', 0.5799865126609802, 'ml-dl', 0), ('explosion/thinc', 0.5792794823646545, 'ml-dl', 3), ('mosaicml/composer', 0.5748881101608276, 'ml-dl', 4), ('pytorch/botorch', 0.5717622637748718, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5676509141921997, 'ml', 3), ('tlkh/tf-metal-experiments', 0.5673794746398926, 'perf', 1), ('cvxgrp/pymde', 0.5655942559242249, 'ml', 2), ('aws/sagemaker-python-sdk', 0.5633373856544495, 'ml', 2), ('pytorch/pytorch', 0.5628584623336792, 'ml-dl', 3), ('blackhc/toma', 0.5623881220817566, 'ml-dl', 2), ('microsoft/deepspeed', 0.5605365037918091, 'ml-dl', 3), ('aistream-peelout/flow-forecast', 0.5596166253089905, 'time-series', 2), ('pyro-ppl/pyro', 0.5561047196388245, 'ml-dl', 3), ('nyandwi/modernconvnets', 0.5554874539375305, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.5545974373817444, 'ml-dl', 1), ('hazyresearch/hgcn', 0.551753580570221, 'ml', 0), ('huggingface/huggingface_hub', 0.5494725108146667, 'ml', 3), ('google/tf-quant-finance', 0.5492528676986694, 'finance', 0), ('karpathy/mingpt', 0.548469066619873, 'llm', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5479029417037964, 'ml', 3), ('davidmrau/mixture-of-experts', 0.5471916198730469, 'ml', 1), ('deci-ai/super-gradients', 0.5459545850753784, 'ml-dl', 3), ('huggingface/evaluate', 0.5455392003059387, 'ml', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5449861288070679, 'study', 0), ('lucidrains/dalle2-pytorch', 0.5412147641181946, 'diffusion', 1), ('d2l-ai/d2l-en', 0.54007488489151, 'study', 3), ('tensorly/tensorly', 0.5390286445617676, 'ml-dl', 2), ('deepmind/dm-haiku', 0.5363015532493591, 'ml-dl', 2), ('graykode/nlp-tutorial', 0.5355981588363647, 'study', 1), ('speechbrain/speechbrain', 0.5351871848106384, 'nlp', 2), ('dmlc/dgl', 0.5350419878959656, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5337355136871338, 'study', 3), ('qdrant/quaterion', 0.5309311747550964, 'ml', 3), ('huggingface/optimum', 0.5307228565216064, 'ml', 1), ('huggingface/diffusers', 0.528372049331665, 'diffusion', 2), ('tensorflow/addons', 0.5261369943618774, 'ml', 3), ('ddbourgin/numpy-ml', 0.5257222652435303, 'ml', 1), ('huggingface/datasets', 0.523903489112854, 'nlp', 3), ('optimalscale/lmflow', 0.5235216617584229, 'llm', 2), ('koaning/human-learn', 0.522743284702301, 'data', 1), ('salesforce/deeptime', 0.5224276185035706, 'time-series', 1), ('onnx/onnx', 0.521939218044281, 'ml', 4), ('humancompatibleai/imitation', 0.5217827558517456, 'ml-rl', 0), ('facebookresearch/dinov2', 0.5208505988121033, 'diffusion', 0), ('google-research/deeplab2', 0.5203951597213745, 'ml', 0), ('huggingface/peft', 0.5194254517555237, 'llm', 1), ('timdettmers/bitsandbytes', 0.5188927054405212, 'util', 0), ('tensorflow/data-validation', 0.5164223313331604, 'ml-ops', 0), ('kubeflow/fairing', 0.5159615874290466, 'ml-ops', 0), ('neuralmagic/deepsparse', 0.5143234133720398, 'nlp', 0), ('rwightman/pytorch-image-models', 0.512000322341919, 'ml-dl', 1), ('tensorflow/similarity', 0.5106225609779358, 'ml-dl', 2), ('facebookresearch/theseus', 0.5099302530288696, 'math', 2), ('keras-rl/keras-rl', 0.5081517696380615, 'ml-rl', 1), ('udacity/deep-learning-v2-pytorch', 0.5055270195007324, 'study', 3), ('keras-team/autokeras', 0.5053526759147644, 'ml-dl', 2), ('ray-project/ray', 0.504410982131958, 'ml-ops', 3), ('jeshraghian/snntorch', 0.5029792189598083, 'ml-dl', 2), ('kornia/kornia', 0.5025068521499634, 'ml-dl', 4), ('calculatedcontent/weightwatcher', 0.5007023811340332, 'ml-dl', 0), ('aiqc/aiqc', 0.5004318356513977, 'ml-ops', 0)]

data

https://github.com/lk-geimfari/mimesis

[]

[]

[]

lk-geimfari/mimesis

mimesis

Python

https://mimesis.name

Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.

lk-geimfari

2024-01-14

2016-09-09

Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.

['api-mock', 'data', 'dataframe', 'datascience', 'dummy', 'fake', 'faker', 'fixtures', 'generator', 'json', 'json-generator', 'mimesis', 'mock', 'pandas', 'polars', 'schema', 'syntetic', 'synthetic-data', 'testing']

['api-mock', 'data', 'dataframe', 'datascience', 'dummy', 'fake', 'faker', 'fixtures', 'generator', 'json', 'json-generator', 'mimesis', 'mock', 'pandas', 'polars', 'schema', 'syntetic', 'synthetic-data', 'testing']

2024-01-12

[('joke2k/faker', 0.6268561482429504, 'data', 3), ('getsentry/responses', 0.5823182463645935, 'testing', 0), ('python-odin/odin', 0.5751269459724426, 'util', 1), ('pytoolz/toolz', 0.5705004334449768, 'util', 0), ('marshmallow-code/marshmallow', 0.5606078505516052, 'util', 1), ('asacristani/fastapi-rocket-boilerplate', 0.5495461225509644, 'template', 0), ('snyk/faker-security', 0.5424483418464661, 'security', 0), ('fastai/fastcore', 0.5298691987991333, 'util', 0), ('pytables/pytables', 0.5296199321746826, 'data', 0), ('eleutherai/pyfra', 0.5290538668632507, 'ml', 0), ('nedbat/coveragepy', 0.5268024206161499, 'testing', 0), ('kubeflow/fairing', 0.5213274955749512, 'ml-ops', 0), ('falconry/falcon', 0.5205872654914856, 'web', 0), ('pyeve/cerberus', 0.5154099464416504, 'data', 0), ('brokenloop/jsontopydantic', 0.5148674845695496, 'util', 0), ('dagworks-inc/hamilton', 0.5131853222846985, 'ml-ops', 2), ('pypy/pypy', 0.5108677744865417, 'util', 0), ('unionai-oss/pandera', 0.509531557559967, 'pandas', 3), ('pandas-dev/pandas', 0.5089730024337769, 'pandas', 2), ('jsonpickle/jsonpickle', 0.5048171281814575, 'data', 1), ('tiangolo/sqlmodel', 0.5006476640701294, 'data', 1)]

ml

https://github.com/apple/coremltools

[]

[]

[]

apple/coremltools

coremltools

Python

https://coremltools.readme.io

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

apple

2024-01-14

2017-06-30

https://avatars.githubusercontent.com/u/10639145?v=4

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

['coreml', 'coremltools', 'machine-learning', 'model-conversion', 'model-converter', 'pytorch', 'tensorflow']

['coreml', 'coremltools', 'machine-learning', 'model-conversion', 'model-converter', 'pytorch', 'tensorflow']

2024-01-10

[('huggingface/exporters', 0.6746289730072021, 'ml', 6), ('microsoft/nni', 0.5904099345207214, 'ml', 3), ('huggingface/datasets', 0.5799334049224854, 'nlp', 3), ('polyaxon/polyaxon', 0.5726978778839111, 'ml-ops', 3), ('selfexplainml/piml-toolbox', 0.558883786201477, 'ml-interpretability', 0), ('districtdatalabs/yellowbrick', 0.5530053377151489, 'ml', 1), ('keras-team/autokeras', 0.546789288520813, 'ml-dl', 2), ('deepchecks/deepchecks', 0.539066731929779, 'data', 2), ('lucidrains/toolformer-pytorch', 0.5380272269248962, 'llm', 0), ('nccr-itmo/fedot', 0.5334883332252502, 'ml-ops', 1), ('mosaicml/composer', 0.5304659605026245, 'ml-dl', 2), ('mlflow/mlflow', 0.5302109122276306, 'ml-ops', 1), ('onnx/onnx', 0.5105490684509277, 'ml', 3), ('wandb/client', 0.510352373123169, 'ml', 3), ('lutzroeder/netron', 0.5053067803382874, 'ml', 4), ('kubeflow/fairing', 0.5047186613082886, 'ml-ops', 0)]

ml

https://github.com/microsoft/flaml

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microsoft/flaml

FLAML

Jupyter Notebook

https://microsoft.github.io/FLAML/

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

microsoft

2024-01-13

2020-08-20

https://avatars.githubusercontent.com/u/6154722?v=4

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

['automated-machine-learning', 'automl', 'classification', 'data-science', 'deep-learning', 'finetuning', 'hyperparam', 'hyperparameter-optimization', 'jupyter-notebook', 'machine-learning', 'natural-language-generation', 'natural-language-processing', 'random-forest', 'regression', 'scikit-learn', 'tabular-data', 'timeseries-forecasting', 'tuning']

['automated-machine-learning', 'automl', 'classification', 'data-science', 'deep-learning', 'finetuning', 'hyperparam', 'hyperparameter-optimization', 'jupyter-notebook', 'machine-learning', 'natural-language-generation', 'natural-language-processing', 'random-forest', 'regression', 'scikit-learn', 'tabular-data', 'timeseries-forecasting', 'tuning']

2023-11-29

[('mljar/mljar-supervised', 0.7940219044685364, 'ml', 7), ('microsoft/nni', 0.7865293025970459, 'ml', 6), ('keras-team/autokeras', 0.7519674897193909, 'ml-dl', 4), ('awslabs/autogluon', 0.7324180603027344, 'ml', 9), ('automl/auto-sklearn', 0.7281423807144165, 'ml', 4), ('shankarpandala/lazypredict', 0.6851475834846497, 'ml', 4), ('winedarksea/autots', 0.6591876745223999, 'time-series', 3), ('ray-project/tune-sklearn', 0.6497718095779419, 'ml', 2), ('nccr-itmo/fedot', 0.6372272372245789, 'ml-ops', 4), ('epistasislab/tpot', 0.6104899048805237, 'ml', 7), ('kubeflow/katib', 0.6054434776306152, 'ml', 0), ('featurelabs/featuretools', 0.6044269800186157, 'ml', 5), ('karpathy/micrograd', 0.5962915420532227, 'study', 0), ('determined-ai/determined', 0.5710462927818298, 'ml-ops', 4), ('huggingface/datasets', 0.5613746047019958, 'nlp', 3), ('alpa-projects/alpa', 0.5595440864562988, 'ml-dl', 2), ('ray-project/ray', 0.5589168667793274, 'ml-ops', 5), ('rafiqhasan/auto-tensorflow', 0.5583081841468811, 'ml-dl', 2), ('google/pyglove', 0.5550763607025146, 'util', 2), ('alkaline-ml/pmdarima', 0.5537622570991516, 'time-series', 1), ('huggingface/evaluate', 0.5472498536109924, 'ml', 1), ('ggerganov/ggml', 0.5470587611198425, 'ml', 1), ('salesforce/merlion', 0.5453396439552307, 'time-series', 2), ('google/vizier', 0.5446196794509888, 'ml', 4), ('firmai/atspy', 0.5425217747688293, 'time-series', 0), ('huggingface/autotrain-advanced', 0.5361581444740295, 'ml', 3), ('uber/petastorm', 0.5358170866966248, 'data', 2), ('rasbt/mlxtend', 0.5349463224411011, 'ml', 2), ('scikit-optimize/scikit-optimize', 0.5336490869522095, 'ml', 3), ('xplainable/xplainable', 0.5324146747589111, 'ml-interpretability', 2), ('autoviml/auto_ts', 0.5320088863372803, 'time-series', 1), ('hiyouga/llama-factory', 0.5290379524230957, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5290378928184509, 'llm', 0), ('rasbt/machine-learning-book', 0.5261116623878479, 'study', 3), ('tensorflow/tensor2tensor', 0.5255224704742432, 'ml', 2), ('neuralmagic/sparseml', 0.525197446346283, 'ml-dl', 1), ('ashleve/lightning-hydra-template', 0.5247229337692261, 'util', 1), ('koaning/human-learn', 0.5246617197990417, 'data', 2), ('explosion/thinc', 0.5239095091819763, 'ml-dl', 3), ('tensorflow/data-validation', 0.5238473415374756, 'ml-ops', 0), ('huggingface/transformers', 0.5236698985099792, 'nlp', 3), ('gradio-app/gradio', 0.5217346549034119, 'viz', 3), ('aws/sagemaker-python-sdk', 0.5215294361114502, 'ml', 1), ('wandb/client', 0.5212470293045044, 'ml', 4), ('tigerlab-ai/tiger', 0.5200607180595398, 'llm', 1), ('teamhg-memex/eli5', 0.5194395184516907, 'ml', 3), ('ourownstory/neural_prophet', 0.5170196294784546, 'ml', 2), ('districtdatalabs/yellowbrick', 0.5168565511703491, 'ml', 2), ('patchy631/machine-learning', 0.5163537263870239, 'ml', 0), ('nixtla/statsforecast', 0.5162665843963623, 'time-series', 3), ('ml-tooling/opyrator', 0.5143886804580688, 'viz', 1), ('sktime/sktime', 0.5142588019371033, 'time-series', 3), ('optuna/optuna', 0.5139668583869934, 'ml', 2), ('catboost/catboost', 0.5135185122489929, 'ml', 2), ('oml-team/open-metric-learning', 0.5119624733924866, 'ml', 2), ('google/temporian', 0.511090874671936, 'time-series', 0), ('linkedin/greykite', 0.5093781352043152, 'ml', 0), ('ageron/handson-ml2', 0.5074008107185364, 'ml', 0), ('pycaret/pycaret', 0.5066448450088501, 'ml', 4), ('selfexplainml/piml-toolbox', 0.5052233338356018, 'ml-interpretability', 0), ('huggingface/peft', 0.5017809867858887, 'llm', 0), ('merantix-momentum/squirrel-core', 0.5003235340118408, 'ml', 4), ('polyaxon/polyaxon', 0.5002272129058838, 'ml-ops', 4)]

nlp

https://github.com/neuralmagic/deepsparse

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neuralmagic/deepsparse

deepsparse

Python

https://neuralmagic.com/deepsparse/

Sparsity-aware deep learning inference runtime for CPUs

neuralmagic

2024-01-13

2020-12-14

https://avatars.githubusercontent.com/u/68670575?v=4

Sparsity-aware deep learning inference runtime for CPUs

['computer-vision', 'cpus', 'deepsparse', 'inference', 'llm-inference', 'machinelearning', 'nlp', 'object-detection', 'onnx', 'performance', 'pretrained-models', 'pruning', 'quantization', 'sparsification']

['computer-vision', 'cpus', 'deepsparse', 'inference', 'llm-inference', 'machinelearning', 'nlp', 'object-detection', 'onnx', 'performance', 'pretrained-models', 'pruning', 'quantization', 'sparsification']

2024-01-10

[('neuralmagic/sparseml', 0.7135436534881592, 'ml-dl', 5), ('microsoft/deepspeed', 0.643064022064209, 'ml-dl', 1), ('microsoft/onnxruntime', 0.6196989417076111, 'ml', 1), ('alpa-projects/alpa', 0.6077420711517334, 'ml-dl', 0), ('lutzroeder/netron', 0.5835353136062622, 'ml', 2), ('huggingface/datasets', 0.5752450823783875, 'nlp', 2), ('tlkh/tf-metal-experiments', 0.5730476379394531, 'perf', 0), ('mosaicml/composer', 0.5704326033592224, 'ml-dl', 0), ('squeezeailab/squeezellm', 0.5691953301429749, 'llm', 1), ('intel/intel-extension-for-pytorch', 0.5653654932975769, 'perf', 1), ('bigscience-workshop/petals', 0.5630180835723877, 'data', 2), ('keras-team/keras', 0.5612209439277649, 'ml-dl', 0), ('onnx/onnx', 0.5601222515106201, 'ml', 1), ('aiqc/aiqc', 0.556576669216156, 'ml-ops', 0), ('nvidia/deeplearningexamples', 0.554892361164093, 'ml-dl', 2), ('determined-ai/determined', 0.5497815608978271, 'ml-ops', 0), ('huggingface/optimum', 0.5494052171707153, 'ml', 3), ('vllm-project/vllm', 0.5437913537025452, 'llm', 1), ('nyandwi/modernconvnets', 0.5420705080032349, 'ml-dl', 1), ('explosion/thinc', 0.5401971936225891, 'ml-dl', 1), ('apache/incubator-mxnet', 0.53566974401474, 'ml-dl', 0), ('facebookresearch/ppuda', 0.5350483059883118, 'ml-dl', 0), ('tensorflow/tensorflow', 0.5337933301925659, 'ml-dl', 0), ('huggingface/transformers', 0.532548189163208, 'nlp', 2), ('tensorflow/tensor2tensor', 0.5306204557418823, 'ml', 0), ('rwightman/pytorch-image-models', 0.5303251147270203, 'ml-dl', 1), ('horovod/horovod', 0.5292316675186157, 'ml-ops', 1), ('roboflow/supervision', 0.5291524529457092, 'ml', 2), ('deepfakes/faceswap', 0.5284426212310791, 'ml-dl', 0), ('ddbourgin/numpy-ml', 0.5217757225036621, 'ml', 0), ('pytorch/glow', 0.514583945274353, 'ml', 0), ('pytorch/ignite', 0.5143234133720398, 'ml-dl', 0), ('calculatedcontent/weightwatcher', 0.5087005496025085, 'ml-dl', 0), ('paddlepaddle/paddle', 0.5083123445510864, 'ml-dl', 0), ('rasbt/machine-learning-book', 0.5080384612083435, 'study', 0), ('blackhc/toma', 0.5067216753959656, 'ml-dl', 0), ('cvxgrp/pymde', 0.5062499642372131, 'ml', 0), ('pytorchlightning/pytorch-lightning', 0.5051679015159607, 'ml-dl', 0), ('megvii-basedetection/yolox', 0.5051378607749939, 'ml', 2), ('polyaxon/polyaxon', 0.504664957523346, 'ml-ops', 0), ('ludwig-ai/ludwig', 0.5014819502830505, 'ml-ops', 2), ('facebookresearch/pytorch3d', 0.5011691451072693, 'ml-dl', 0), ('fepegar/torchio', 0.5005324482917786, 'ml-dl', 0)]

data

https://github.com/sqlalchemy/alembic

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sqlalchemy/alembic

alembic

Python

A database migrations tool for SQLAlchemy.

sqlalchemy

2024-01-13

2018-11-27

https://avatars.githubusercontent.com/u/6043126?v=4

A database migrations tool for SQLAlchemy.

['sql', 'sqlalchemy']

['sql', 'sqlalchemy']

2024-01-13

[('sqlalchemy/sqlalchemy', 0.8273295164108276, 'data', 2), ('agronholm/sqlacodegen', 0.6634976267814636, 'data', 0), ('mause/duckdb_engine', 0.6483481526374817, 'data', 2), ('tiangolo/sqlmodel', 0.6223205924034119, 'data', 2), ('aminalaee/sqladmin', 0.5552855730056763, 'data', 1), ('ibis-project/ibis', 0.5510525107383728, 'data', 2), ('aeternalis-ingenium/fastapi-backend-template', 0.5487288236618042, 'web', 1), ('mcfunley/pugsql', 0.5098458528518677, 'data', 1)]

util

https://github.com/pypa/setuptools

['setuptools', 'build']

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pypa/setuptools

setuptools

Python

https://pypi.org/project/setuptools/

Official project repository for the Setuptools build system

pypa

2024-01-12

2016-03-29

https://avatars.githubusercontent.com/u/647025?v=4

Official project repository for the Setuptools build system

[]

['build', 'setuptools']

2024-01-11

[('pyo3/setuptools-rust', 0.6672810912132263, 'util', 2)]

pandas

https://github.com/delta-io/delta-rs

['databricks', 'rust']

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delta-io/delta-rs

delta-rs